Namour Ph

An approach to pathogens flux simulation in a combined sewer system Une approche de la simulation des pathogènes dans un réseau unitaire

Keywords: Combined sewer, Hydrological indicators, Pathogens, Sewer overflow

Les collecteurs d'assainissement unitaires transportent des matières organiques et des pathogènes issus des déjections humaines et animales. Ces matières sont mélangées avec le ruissellement lors des événements pluvieux et peuvent sur-verser dans les milieux aquatiques naturels. Les premières analyses génétiques ont été réalisées sur un petit cours d'eau expérimental et son réseau unitaire latéral (site OTHU). Les résultats indiquent que les pathogènes du milieu naturel sont une évolution des pathogènes du réseau. Toutefois la survie présumée ne doit pas être confondue avec la fréquence de leurs apports au cours d'eau. Une première étape consiste à vérifier les concentrations et les fréquences auxquelles ils sont délivrés au milieu récepteur. Les premières données sur trois espèces de pathogènes ont été échantillonnées de 2009 à 2011 dans le collecteur unitaire. Les données montrent des variations qui peuvent être reliées aux séquences de temps sec et humide. Toutefois les variations entre les espèces pathogènes ne sont pas totalement corrélées. Cela pourrait indiquer des taux de croissance et/ou des localisations différentes dans le collecteur d'assainissement. L'hypothèse principale pour expliquer l'augmentation des concentrations avec les évènements de pluie est l'arrachage des bio-films organiques qui tapissent la partie immergée de la conduite d'assainissement. Cette hypothèse est à confirmer par des données complémentaires et plus ciblées sur la colonne d'eau en temps de pluie et les tapis de bio-films en temps sec.

PROBLEMATIC

From the literature [START_REF] Tait | Modelling In-Sewer Deposit Erosion to Predict Sewer Flow Quality[END_REF][START_REF] Mohanakrishnan | Variation in Biofilm Structure and Activity Along the Length of a Rising Main Sewer[END_REF][START_REF] Pai | Monitoring and assessing variation of sewage quality and microbial functional groups in a trunk sewer line[END_REF] it is known that organic compounds and biofilms can develop in the immersed part of a sewer pipe, resulting from nutrient-rich water conveyed during dry weather periods, and also from a quite stable water temperature of 12 °C all over the year long. It results in a mineral and organic layer which is partially scraped from the immersed part of pipe during storm events, when runoff enters the sewer systems, increasing flow velocity and shear stress. Combined sewer overflows (CSOs) are loaded with pollution including pathogens. A field experiment [START_REF] Tait | Modelling In-Sewer Deposit Erosion to Predict Sewer Flow Quality[END_REF] implemented in a combined sewer pipe showed the suspended solid concentration in waters was depending on flow magnitude and was decreasing if a same discharge flow was maintained. Only an increase in discharge was able to provide a new increase in suspended solid, indicating more cohesion of the remaining sediment. A large cohesion of the biofilm and organic matrix complex was also observed [START_REF] Rocher | Biofilm in combined sewers: wet weather pollution source and/or dry weather pollution indicator?[END_REF].

Bacteria linked to human faeces flow with sewer waters but are also known to develop in biofilm on and into organic deposits taking place on immersed part of sewer pipes [START_REF] Ghazani | Biofilm formation of Escherichia coli O111 on cement surfaces[END_REF][START_REF] Soleimani | Growth and characterization of Escherichia coli DH5α biofilm on concrete surfaces as a protective layer against microbiologically influenced concrete deterioration (MICD)[END_REF]. The amount of bacteria can be close to that of activated sludge (Chen et al., 2003). Faecal bacteria seem to be more abundant close to bottom pipe communities [START_REF] Gomez-Alvarez | Metagenome analyses of corroded concrete wastewater pipe biofilms reveal a complex microbial system[END_REF] when the density of the live population of E. coli DH5α is the highest at three fourths of the biofilm depth. The structural heterogeneity in a biofilm is explained both by shear flow velocity at biofilm surface and pH at concrete surface. These results help understand why bacteria amount can varying in sewage water during storm events [START_REF] Stewart | Biofilm structural heterogeneity visualized by three microscopic methods[END_REF]Chen et al., 2003;[START_REF] Leung | Effect of detached/re-suspended solids from sewer sediment on the sewage phase bacterial activity[END_REF].

To study the fate of pathogens delivered by CSOs in a small stream, ten water sampling were collected from a combined sewer pipe between October 2009 and September 2011. Calculated intestinal enterococci, E. coli and P. aeruginosa concentrations showed large variations between samples. It appeared that each bacterial concentration could change from one order of magnitude and were not varying in the same way [START_REF] Petit | Ecologie et dangerosité des Pseudomonas aeruginosa des milieux aquatiques anthropisés[END_REF]. This would influence notably their dissemination into receiving waters and then contribute to the understanding of their population fluctuations in the natural system.

The objective of this work is to assess if there are correlations between bacteria-concentrations and antecedent flow conditions. The following two hypotheses are tested: H1: During dry weather periods lower pathogen concentrations should be observed in the sewage water. At the same time biofilm is expected to grow. H2: During and just after flushing events the scouring of lining biofilm should induce a rapid increase in pathogens concentrations into the water column.

These two hypotheses can influence each other: the longer the dry period, the greater the potential to deliver larger amount of pathogens for a subsequent storm event and vice versa.

MATERIAL AND METHODS



Combined Sewer Overflow (CSO) time series

The drained impervious area is of 0.16 km 2 at the CSO unit (see Fig. 1). This unit overflows when discharge in the sewer is exceeding 0.010 m 3 /s, almost five folds the mean dry weather discharge of 0.0018 m 3 /s. Overflow is recorded thanks to a gauge station which provides a flow time series over the period of interest (2009 to 2011) at a fine time step. CSO peaks median value is of 0.01 m 3 /s with a median CSO events duration of 17 minutes. Events occur 113 days over 718, several time a day with a mean overflow frequency of 0.44 day. From these summary statistics there are clearly dominant periods without overflows, called dry periods and others concentrating overflows, called wet period. Overflow events are considered to be indicators of hydraulic stresses that would explain the observed pathogen concentration variability at the sampling site.

 Hydrological Indicators (HIs)

To test different aspects of hydraulic constraints like magnitude, duration and frequency, several hydrological indicators (HI) were defined. These indicators, previously tested in another experiment (Breil et al., 2007), are summarized into 11 flow characteristics. Each HI is computed from the CSO time series over a preceding period-duration (PPDs) to account for hydraulic history. PPDs range from 1 to 15 days before each date of sampling. Their potential effects on pathogens concentration in the sewage water are indicated in Table 1.



Microbiological data

Samples were collected from the sewer water column [START_REF] Petit | Ecologie et dangerosité des Pseudomonas aeruginosa des milieux aquatiques anthropisés[END_REF]. Analyses were performed to detect intestinal enterococci, E. coli and P. aeruginosa. Some statistics are presented in Table 2. E.Coli is the most abundant followed by intestinal enterococci and then P.aeruginosa. There are three orders of magnitude between first and last pathogen species mean values. It can indicate the pathogen developments are different in the sewer. A Wilcoxson non-parametric test to account for deviance from normal distributions was performed on rank-data. It confirmed the pathogens exhibit very different distributions (two-sided probabilities less than 0.007).

The lowest relative dispersion is observed for intestinal enterococci with a mean value of 0.8. Dispersion is not dependant on mean pathogen concentration. Other factors linked to each pathogen species would explain these differences. Not limited to, there are the population rates of growth, their preference for specific conditions and then locations in the sewer network and their strength of attachment to the biofilm.

The three species positively but only partially correlate (see Table 3) indicating they do not fully fluctuate in the same way. P. aeruginosa (Pae) is most connected to Escherichia coli (EC). This would indicate a closer behaviour than with intestinal enterococci (IE), which seems independent.



Relationships between bacterial concentrations and hydrological indicators.

Considering the limited number of biotic data, the observed non-normal distribution of variables, in particular for hydrological data containing tied data, the Kendall's Tau test was used. Only significant Tau values (U 0.975 ) are discussed in the results item.

 Hierarchical clustering of each HI contribution to biotic indicators variability

Multi-linear regression analyses were performed. The eleven hydrological indicators were computed (see Table 3) meaning 1 more than the number of data. It means resulting model would not be efficient at all. Then a stepwise regression procedure was used to keep only more relevant hydrological indicators, controlling for co-linearity effects, rejecting on necessary already entered variables. As the method works with normal distributions, biotic and hydrological indicators were log transformed with (x+1) to account for zero values. Overall most of the hydrological indicators were not really adapted even using for example the sophisticated Box-Cox transformation. This was due to tied data inherent to the sampling process and definition of most of the HIs. Only PPDs of 1, 2, 3, 5, 10 and 15 were used. All adjusted R2 values were near 1 and greater than any individual correlation. All coefficients were checked to be significant with a p-value of less than 0.05. No cross correlations between selected independent variables was greater than 0.6 when a threshold of 0.8 (Klein rule) is recommended. Also signs of contributions to the model were compared to signs of direct correlations with bacteria concentration, as to detect "compensation effects". It happens at least for the third variable with P. aeruginosa and forth variables for the two others bacteria. It is also considered that by

 P.aeruginosa

Seven hydrological indicators exhibit significant correlation with P. aeruginosa concentrations in sewage waters. However, only three are not redundant and are reported in Figure 2.

The maximum overflow duration (HI-2) recorded in the 4-5 days prior dates of sampling correlates negatively with P.aeruginosa concentration in the sewage water. This seems to result from the cleaning effect of long rain events that rinse the sewer network as it can be observed on CSOs electrical conductivity for such hydraulic events.

Positive Tau values are observed for total time without CSOs (HI-10) occurring in the 4-5 days before date of sampling. Long dry sequences allow growing of biofilm with the delivering of more P.aeruginosa bacteria in the sewage water when a hydraulic perturbation occurs. A PPD of 4-5 days tends to indicate this bacteria species gets a fast growing rate.

Finally, for a PPD of 13-14 days, the longer the time-duration between maximum CSO peak (HI-5) and date of sampling, the higher the concentration of P.aeruginosa at this date. This result does not contradict hypothesis H1 because smaller hydraulic events can occur between maximum CSO peak and date of sampling. This result probably indicates large hydraulic events have the ability to weaken the biofilm cohesion for subsequent smaller events. Large events seem to occur on a two weeks basis in this case. All selected HIs with their correlation signs well support the formulated hypotheses H1 and H2.



Intestinal enterococci

Only two HIs have significant Tau values with intestinal enterococci concentrations (Fig. 3). They are negative and concern hydraulic conditions that occurred in the day prior date of sampling. This result clearly indicates independence of intestinal enterococci concentration from hydraulic conditions that occurred in previous days. No interpretation can be derived from HI-5 and HI-10 in this case. Intestinal enterococci pathogens exhibit an intermediate abundance with the smaller dispersion around its mean value (see table 1). This would confirm that there is no specific storage location for this pathogen in the sewer system other than in the water column.

 Escherichia coli

In that case, six HIs have significant correlation with E.coli concentrations in sewage waters. Only two really differ and are presented in Figure 4. They both exhibit a correlation peak at 9 days but with opposite signs.

With duration of maximum CSO (HI-2), the negative correlation can be interpreted as the remnant effect of sewer flushing by a flood event in the sewer. This is the same effect than for P.aeruginosa but it requires a longer PPD of 9 days against 5 to be detected. It means the delivery of E.coli is more persistent in time after a flushing event. It can be due to a larger quantity of this pathogen species in the biofilm considering its mean concentration is the highest in the sewage water (see table 1). The long duration of 9 days could also indicate more resistance to hydraulic scraping of this species, meaning it not located on biofilm surface but in the deeper and oxygen most depleted layers. This possibility is in agreement with observations done by several authors [START_REF] Rocher | Biofilm in combined sewers: wet weather pollution source and/or dry weather pollution indicator?[END_REF][START_REF] Gomez-Alvarez | Metagenome analyses of corroded concrete wastewater pipe biofilms reveal a complex microbial system[END_REF].

Concerning duration of the longest dry period (HI-9), it has more or less the same effect than HI-10 on P.aeruginosa concentrations. The positive correlation would indicate the release of E.coli from developed biofilms after suitable conditions for its growing and with small hydraulic events.



Intermediate conclusion

Overall, what is are the time durations to detect significant correlations of bacteria concentrations with HIs, or the absence of correlations with given HIs. On one side, there are negative correlations that can be compared to a remnant effect of perturbations which is to decrease bacteria concentrations because of the repeated biofilm reduction and possibly not enough time for regeneration. On the other side, there are positive correlations that can correspond to periods without perturbations and then to an increase in concentrations when a hydraulic event occurs. So, depending on the antecedent conditions, we could get either increasing or decreasing of bacteria concentrations.

Intestinal enterococci seems to decrease rapidly, on a short term, with no perturbation event, which tends to demonstrate it mainly stands in the water column with no other sources than sewage waters coming to the sewer. This could explain the fact that there were no significant positive correlations and only two hydrological indicators in overall correlations.

P. aeruginosa and E. coli, seems to follow a same behaviour which is confirmed in table 2. Relevant hydrological indicators are quite the same with same explanations. However, they differ in term of PPD to detect correlations. The difference from 5 to 9 days between these two pathogen species would indicate E.coli is more resistant to flushing because it develops at closer to the pipe, in the more anoxic layers as it was observed by some authors (e.g. [START_REF] Soleimani | Growth and characterization of Escherichia coli DH5α biofilm on concrete surfaces as a protective layer against microbiologically influenced concrete deterioration (MICD)[END_REF].

Multi-linear Regression Analysis

As a result of the combination of observed positive and negative effects of HIs on the pathogen concentrations in the sewage water, there are possibly some indirect effects with hidden influences not detectable using single correlation approaches. To check for these potential effects, multi-linear regressions were performed. That kind of model helps to identify contribution and hierarchical rank of each HI in variability of biotic indicators. Then some causal analysis can be conducted from the models if it well represent the observed variability, meaning an adjusted R2 greater than 0.9.

Results are listed in table 4 with selected independent variables, given in order of contribution with their sign to multi-linear models (first row is the most contributing), with their period (PPD) of detection, and their meaning for interpretation.

Seven over eleven HIs were selected by the stepwise regression process. Among them, dry time with different PPDs and sign is the first. Also noticeable are CSO related His (HI-1, HI-7, HI-8) in the selected independent variables.

 Intestinal enterococci

First HI is the duration time without runoff in the sewer system just before the maximum observed CSO in the period of calculation. It correlates negatively, considering here the day before each sample date (10 samples were performed). Previously, it was displayed that this bacteria was linked to short term HIs like time to maximum (H-I5) and number of dry sequences (HI-11) (Figure 2). These observations can indicate that intestinal enterococci mainly present in the sewer water and then react to short term modifications. For example on a 1 day duration, longer is the duration before maximum observed CSO (in the day) before sampling, closer from sampling is the runoff event that induces dilution.

Second HI selected is maximum CSO in the preceding 10 days. Again it has a negative effect on bacterial concentration. The concentration decreasing would then be attributed to a large flow occurring in the sewer with somewhat like a cleaning effect.

Third HI is the total CSO duration over a PPD of 5 days. It is the only HI having a positive effect on the intestinal enterococci concentration. Longer is the duration, longer is the dilution of water by rainfallrunoff and we then do not expect an increase in bacterial concentration living mainly in the water column.

This result is not consistent with the expected role of the two first variables in the model, which is questioning and would indicate a mathematical compensation effect.

 E. Coli

First HI is the same than for the two others bacteria but it correlates positively with E. coli, for a PPD of 3 days. Referring to our initial assumption, E.coli is expected to develop in priority in the attached biofilm lining the pipe. So, the longer the dry sequence, the more we get bacteria in the water after a flushing event.

Second is number of CSOs in the preceding two days. Again, the explanation can be: the more flushing occurs, the more biofilm provides bacteria.

Third (not presented in table 1) is per cent of sewage water with a negative contribution to the model. This result is in agreement with the assumption E.coli mainly develops in the attached biofilm because for less dilution we have, less bacteria are in the sewer water.

 P. aeruginosa

First HI is the same for the two others bacteria, but with a negative correlation for a PPD of 3 days. Referring to our initial assumption, it seems this bacterium has the same behaviour of intestinal enterococci, meaning it develops in the water column rather than in the attached biofilm. However global correlations between bacterial species (Table 2) do not indicate such a link.

Second HI is the duration from date of sampling to maximum CSO that occurred in the PPD, here of 10 days. The contribution to the model is positive, indicating that: the longer the time from the maximum CSO, the more bacteria are present in the sewage water. It tends to confirm the first HI interpretation.

Third HI is maximum CSO peak in the preceding 2 days. The positive contribution tends to indicate scouring of biofilm, which is not in agreement with the two first HIs interpretation. This again leads to question a mathematical compensation effect.

Overall, third variables seem difficult to interpret in such multi-linear models. Referring to the two first variables in each model, we can observed that intestinal enterococci has a short and long term responses (1 and 10 days), E.coli remains on the short term (3 and 2 days) and P.aeruginosa has also short (3) and long (10) terms responses.

Model characteristics are then considered keeping no more than 4 HIs with coefficient probability less than 0.05 (two side test). They are provided in table 5. We can note the adjusted R 2 values are acceptable and would be used to get a prediction of pathogens release from CSOs. It can be generated calculating for each time step the corresponding HIs and then affecting them with the given coefficients.

Table 5. Multi-linear regression coefficients and their probability level

CONCLUSION

These are preliminary results combining pathogens concentration and hydraulic past conditions. It seems that intestinal enterococci do not have the behaviour of E.coli and P.aeruginosa. This was not expected and assumptions on its location in the sewer pipe have been proposed to explain it.

The final objective is to build a model able to simulated pathogen fluxes that are delivered to a natural system during storm events. To confirm first assumptions stated from this analysis on a limited set of observations, complementary data will be collected in the frame of a running PhD work and in collaboration with Australian specialists. Sampling design is to collect water samples from the water column just before and during flushing events as to catch dynamics of pathogen delivery to the sewer water.

Also dry weather samplings will be performed in the water column in the following days after a flushing event. This will allow checking of the assumption that there is a biological releasing process from biofilms or water-column bio-production in the absence of flushing event.

Grab sampling will also be performed both in the water column and biofilm during wet weather periods to confirm biofilm growth and location of pathogens. Other investigation techniques are foreseen to directly assess biofilm growth rate.

Simulation model will combine growth functions of pathogens as a free process and hydrological indicators as forcing constraints that can positively or negatively influence pathogen concentrations in the sewer water. It will use flow time series to generate a continuous pathogen flux production. Such a model was built to modelize the growth and ageing of macrophyte populations along the years in a large French river (Breil P. et al., 2007). Also water overflows can already be simulated. Pathogens model delivery is expected to run in the coming years.

Figure 1 :

 1 Figure 1: Monitoring site of la Chaudanne (OTHU site)

  and maximum values of analysed pathogens into collected CSS waters.

Figure 2 .

 2 Figure 2. Kendall's Tau rank correlation values with Preceding Period Duration. Triangles: total time without CSOs ( HI-10); Squares: Duration between sampling date and max-CSO (HI-5); Diamonds: Maximum overflow duration (HI-2).

  Figure Kendall's Tau rank correlation values with Preceding Period Duration. Triangles: number of dry sequences (HI-11); Squares: Duration between sampling date and max-CSO (H-5).

Figure 4 .

 4 Figure 4. Kendall's Tau rank correlation with Preceding Period Duration. Squares: Maximum overflow duration (HI-2); Diamonds: Duration of longest dry period (HI-9).

Table 1 .

 1 Hydrological indicators and expected effects on bacterial concentrations

	Id.	Hydrological Indicators (HI)	Unit	Expected effect on pathogens concentrations when a HI increases
	HI-1 Maximum CSO peak	m	3 /s	Increase due to scoring of biofilm
	HI-2 Maximum CSO duration	day	Possible decrease due to rinse effect by rain water
	HI-3 Maximum CSO water amount	Mm	3	Increase if HI-1 is large enough to scrap biofilm
	HI-4 Dry time duration just before HI-1	day	Increase the available amount of biofilm, hence of pathogens
	HI-5 Duration from HI-1 to date of	day	Decrease if no intermediate large CSO occur
		sampling
	HI-6 Number of CSOs			Decrease because there is less time for biofilm to grow
	HI-7 Total CSO duration	day	Possible decrease due to rinse effect by rain water
	HI-8 Total CSOs water amount	Mm	3	Decrease because there is less time for biofilm to grow
	HI-9 Duration of longest dry period	day	Decrease in the absence of CSOs
	HI-10 Total duration of dry periods	day	Decrease in the absence of CSOs
	HI-11 Number of dry periods			Complement to number of CSOs-same effect

Table 3 .

 3 Rank correlation values between concentrations of E. coli (EC), intestinal enterococci (IE) and P.

	aeruginosa (Pae)
	EC	IE	Pae
	EC 1.000
	IE 0.050	1.000
	Pae 0.460	0.075	1.000

Relationships between P.aeruginosa, intestinal enterococci, E.coli and hydrological indicators (HI)

  of the multi-linear regression model, each following variable entering the model is correlated to the remaining residual to the model. Then it is difficult to establish causal interpretation since the third variable. Figures 2, 3 and 4 represent on the horizontal axis the PPD from date of sampling. In each figure large light circles indicate significant Tau values (bilateral test with U 0.975 ).

	E. coli (CFU/100mL) 3 RESULTS AND DISCUSSION	intestinal enterococci (CFU/100mL)	P.aeruginosa (MPN/100mL)
	3.1	number of samples	10	10	9
		minimum	6.51E+05	2.68E+05	7.23E+02
		mean	1.14E+07	2.20E+06	2.89E+04
		maximum	4.17E+07	6.63E+06	9.30E+04
		range	4.10E+07	6.36E+06	9.23E+04
		relative dispersion	1.21E+00	8.30E-01	1.09E+00

definition

Table 4 .

 4 Contribution of hydrological indicators (HI) to the pathogens concentrations variability.

ACKNOWLEDGMENTS

The authors thank the French National Research Agency (INVASION ANR n°08 CESA 022 and EPEC ANR n°10 ECOT 007), ANSES (project PYO-Eau) and FAST-MUROEI bilateral project (France-Australia) for their financial supports.

LIST OF REFERENCES


Nicolas Morin

Julien Camilleri

Cécile Cren-Olivé

Marina Coquery

Cécile Miège
email: cecile.miege@irstea.fr

Determination of uptake kinetics and sampling rates for 56 organic micropollutants using "pharmaceutical" POCIS

Keywords: POCIS, sampling rates, uptake kinetics, calibration system, priority and emerging substances CF, concentration factor, DOC, dissolved organic carbon, DT, degradation time, LC, liquid chromatography, MRM, multiple reaction monitoring, MS, mass spectrometry, POCIS, polar organic chemical integrative sampler, PES, polyethersulfone, PRC, performance and reference compound, PTFE, polytetrafluoroethylene, Rs, sampling rate, RSD, relative standard deviation, SPE, solid-phase extraction, TWA, time-weighted average

The literature increasingly reports sampling rates (Rs) for Polar Organic Chemical Integrative Samplers (POCIS) but the data obtained come from various calibration systems that are not always well-defined (agitation, temperature, measured micropollutant concentrations in water,...). In order to obtain accurate laboratory Rs for priority and emerging substances, POCIS need to be exposed in a robust and well-defined calibration system. Thus, we built a flow-through calibration system containing tap water spiked with 56 organic micropollutants (alkylphenols and phenols, hormones, pesticides, pharmaceuticals, UV filter). POCIS were immersed for up to 28 days. Tap water micropollutant concentrations and additional parameters (temperature, pH, conductivity, dissolved organic carbon, flow velocities) were kept constant and controlled throughout the calibration experiment. Based on the observed uptake kinetics, we distinguished 4 types of micropollutant accumulation patterns: curvilinear accumulation (30 molecules, group 1), accumulation with an inflexion point (13 molecules, group 2), random accumulation (8 molecules, group 3), and no or very low accumulation (5 molecules, group 4). Rs was calculated for 43 out of 56 micropollutants (groups 1 and 2). Calculated Rs values ranged from 0.030 L/d to 0.398 L/d. POCIS can supply TWA

Introduction

Integrative samplers such as the Polar Organic Chemical Integrative Sampler (POCIS) were designed to sample hydrophilic micropollutants [START_REF] Alvarez | Development of an Integrative Sampling Device for Hydrophilic Organic Contaminants in Aquatic Environments[END_REF]. They are immersed for a few days to a few weeks (14 days is a common standard), and accumulate compounds by passive diffusion. They can thus be used for screening with limits of detection that are often better than classic grab sampling since they are able to directly extract micropollutants in situ for several days. POCIS are also claimed to give time-weighted average (TWA) micropollutant concentrations in water over the immersion duration by using accurate sampling rates (Rs). Rs are dependent on environmental parameters such as agitation, temperature or biofouling [START_REF] Alvarez | [END_REF].

TWA concentrations can be produced with in situ Rs but the process entails performing an infield calibration for each campaign, making it a heavily time-intensive method [3], and the micropollutants have to be present in the aquatic system at a relatively constant concentration.

Another method to determine TWA concentrations is to calibrate the POCIS in-lab. The huge advantage of this method is that it only has to be performed once and it allows controlling micropollutant concentrations. However, the drawback is the necessity to correct the in-lab Rs that does not account for the effect of environmental conditions and can thus lead to biased in situ TWA concentrations [4]. This correction is made using internal surrogates, i.e. performance and reference compounds (PRCs), which are currently difficult to identify for POCIS [START_REF] Alvarez | Passive Sampling Techniques in Environmental Monitoring[END_REF].

In order to obtain accurate laboratory Rs, it is necessary to have a reliable, robust and well-defined calibration system. Literature reports do not always detail certain aspects of calibration and calculation, giving at best only partial information on POCIS (type and mass of receiving phase, exposed surface), calibration system (zero, discrete or continuous micropollutant renewal, exposure duration, design of the exposure system, container type, agitation type, physical-chemical parameters, and analyte concentration in the water) or sampling rate calculation method. Furthermore, it is also necessary to control key parameters (temperature, flow velocity, pH, conductivity, dissolved organic carbon (DOC), tap water micropollutant concentrations) and to detail these controls [START_REF] Morin | [END_REF]. Today, numerous references give laboratory Rs with POCIS for micropollutants such as alkylphenols, hormones, pesticides or pharmaceuticals [START_REF] Alvarez | [END_REF][4][START_REF] Alvarez | Passive Sampling Techniques in Environmental Monitoring[END_REF][7][8][9][10][11][12][13][14][15][16][17][18]. But given that lab calibration methods are not performed in the same way nor in a well defined way, Rs could vary widely for a given micropollutant, making it difficult to select a reliable Rs as benchmark.

Here, we report results on kinetic accumulations for 56 priority and emerging micropollutants (8 alkylphenols, 9 hormones, 11 pesticides, 27 pharmaceuticals and 1 UV filter). More specifically, we identify molecules that fit or fail to fit the curvilinear model [START_REF] Alvarez | Development of an Integrative Sampling Device for Hydrophilic Organic Contaminants in Aquatic Environments[END_REF] and go on to discuss Rs calculation method according to molecule. We also give well-defined laboratory Rs produced with the "pharmaceutical" POCIS for 43 micropollutants. All aspects potentially influencing Rs are detailed (i.e. POCIS and calibration system used, characteristics of the exposure media and sampling rate calculation method). We also discuss the validity field of the POCIS according to target micropollutant (concentration factor, optimal exposure duration, possibility for calculating TWA concentrations). We compared our results (accumulation kinetics and Rs) with the literature and studied the influence of log D on Rs.

Material and methods

Chemicals, material and apparatus

Acetonitrile HiPerSolv Chromanorm, Acetonitrile LC/MS HiPerSolv Chromanorm, Dichloromethane HiPerSolv Chromanorm and Methanol HiPerSolv Chromanorm were purchased from VWR (Fontenay-sous-Bois, France). Ultrapure water was obtained on a MilliQ® Advantage A10 system equipped with an LC-Pak cartridge and a 0.22 µm filter Millipak® 40 from Merck-Millipore (Saint-Quentin-en-Yvelines, France). Acetonitrile Chromasolv grade, Acetonitrile LC/MS Chromasolv grade, methanol Chromasolv grade, ammonium acetate puriss p.a. for mass spectroscopy ≥ 99.0%, formic acid puriss p.a. eluent additive for LC-MS ≈ 98%, Ammonium formiate puriss p.a. ≥ 99.0% and acetic acid puriss p.a. ≥ 99.8% were purchased from Fluka (Saint-Quentin-Fallavier, France).

The majority of analytical standards were purchased from Sigma-Aldrich (Saint-Quentin-Fallavier, France), i.e. 7 alkylphenols and phenols (bisphenol A [BPA], t-butylphenol The liquid chromatography-mass spectrometry (LC-MS) system used for the analysis of 10 betablockers and 5 estrogens was composed of an Agilent 1100 chromatographic system from Agilent (Massy, France) coupled with an API 4000 triple-quadrupole mass spectrometer from AB Sciex (Les Ulis, France). The LC-MS system used for the analysis of the 41 remaining molecules was an Agilent 1200 chromatographic system from Agilent (Massy, France) coupled with a triple-quadrupole 3200 Qtrap from AB Sciex (Les Ulis, France).

Calibration design and POCIS exposure

The calibration system is schematized in Figure 1. It consisted of 2 aquaria (up to 50 L) filled by tap water freshly spiked at a nominal value of 3 µg/L for each analyte. This concentration permitted to analyze grab samples by direct injections in LC-MS and thus to easily control this parameter throughout the calibration phase. Triplicates of "pharmaceutical" POCIS (45.8 cm 2 of exposed surface, 200 mg of receiving phase) were immersed for t = 1, 3, 6 and 12 hours and for t = 1, 3, 7, 14, 21 and 28 days. Given that we worked with 2 aquaria with a limited number of POCIS per aquarium and that each exposure duration was tested in triplicate, the total experiment duration was 42 days. In order to closely mimic the agitation conditions found in aquatic rivers, each POCIS was exposed to a current of around 10 cm/s delivered perpendicularly to their surface by a diffusion ramp linked to a submersible pump.

This set-up was inspired by the system developed by Mazzella et al. [10]. Tap water was thermostated at around 20°C by thermostated water-bath using an external thermostated tank.

The system was kept in the dark to prevent any photolysis of analytes.

The whole system was a flow-through calibration system, since freshly-spiked tap water was delivered continuously into each aquarium by a peristaltic pump and the excess was evacuated via an overflow (and sent through a 10 g activated carbon column for clean-up).

Unspiked tap water was contaminated with 2 contaminant solutions (around 100 mg/L each, replaced every week) syringe-pumped into a mixing vessel (which was agitated with a magnetic stirrer). The flow rate from the peristaltic pump permitted to renew 35% of each aquarium per day. Indeed, a previous experiment performed in the same calibration system revealed that 35% of aquarium capacity should be renewed per day in order to have constant concentrations for most molecules (46 out of 56). For the 10 other molecules (identified with asterisks in Figure 1), the degradation percentages were too high and would need excessively high water resources and analytical standards to keep them at a constant 3 µg/L. The flow rate of the syringe pump was set as a function of renewal percentage in order to operate at the nominal concentration.

Insert Figure 1 Before beginning the experiment, 1.5 mL of each contaminant solution was poured into each aquarium in order to obtain a concentration of around 3 µg/L in the tap water.

During the calibration experiment, physical-chemical parameters of the tap water such as temperature, pH, conductivity and DOC were controlled each week in both aquaria. The current velocities at the front of each POCIS surface were also checked every week. To enable sampling rate calculation, water concentrations of micropollutants were monitored twice a week. One sample was collected before the renewal of the contaminant solutions to check potential degradation of micropollutants. A second sample was performed a few hours after the contaminant solutions were renewed to quantify micropollutant concentrations after the system equilibration. Each sample was analyzed in duplicate or in triplicate, and contaminant solutions were analyzed every week.

POCIS and water sample pre-treatment and analysis

POCIS preparation, deployment, retrieval and blank

The POCIS were home-made, with 200 mg (± 5 mg) of receiving Oasis® HLB phase sandwiched between 2 PES membranes. This device set-up was kept between 2 stainless steel rings linked together by screws and nuts. The home-made POCIS were then stored at 5°C (± 1°C) until immersion in the aquaria. During exposure, the POCIS were placed facing the flow coming from the diffusion ramp. After retrieval, the POCIS membranes were rinsed with a few mL of unspiked tap water, and the POCIS were then stored at -23°C (± 3°C) until

extraction. An additional blank POCIS, consisting in a POCIS not immersed in the exposure media was produced in order to check for any contamination of the receiving phase. This blank POCIS was stored at -23°C (± 3°C) until processing.

Treatment of POCIS before analysis

Exposed POCIS and blank were left at ambient temperature for 1 hr before processing.

The POCIS were then disassembled and the sorbent was transferred with a few mL of ultrapure water under low vacuum in pre-weighed 6 mL glass SPE cartridges equipped with PTFE frits. The sorbent was dried under vacuum, and the micropollutants were eluted with 2

x 5 mL of methanol and then 2 x 5 mL of a methanol/dichloromethane mixture (50/50, v/v).

Sorbent was dried again and weighed in order to measure the exact mass analyzed. Each eluate was separated into 3 fractions in order to quantify all micropollutants via 4 analytical methods (one fraction for betablockers analysis, one fraction for estrogen analysis, one fraction for 2 multiresidue analyses on the remaining molecules). The eluates were evaporated to dryness under a gentle stream of N2, and the extracts were then reconstituted into:

-500 µL of a H2O/ACN mixture (99/1, v/v) and 50 µg/L of an internal standard (i.e. metoprolol impurity A) for betablocker analysis, -500 µL of a H2O/ACN mixture (60/40, v/v) and 50 µg/L of an internal standard (i.e.

estradiol acetate) for estrogen analysis, -2 mL of a H2O/ACN mixture (80/20, v/v) for the 2 multiresidue analyses.

Before analysis, extracts were diluted 100 to 500 times in order to be within the concentration range of each method and to guard against matrix effects. For betablocker and hormone analyses, dilutions were done in their respective mobile phase mixtures. For multiresidue analyses, extracts were diluted in ultrapure water. All extracts were stored at -23°C (± 3°C) until analysis.

Treatment of water samples before analysis

The relatively high concentrations of micropollutants in spiked tap water (i.e. around 3 µg/L) made it possible to analyze water samples by direct injection in the chromatographic system after moderate dilution to obtain the adequate mobile phase mixture (i.e. H2O/ACN (99/1, v/v) for betablockers, H2O/ACN (60/40, v/v) for estrogens, and ultrapure water only for multiresidue analyses) and after adding possible internal standards (metoprolol impurity A and estradiol acetate at 50 µg/L for betablocker and estrogen analyses, respectively). Water samples were kept at -23°C (± 3°C) until analysis.

LC-MS/MS analysis

The methods used for betablockers and estrogens are detailed elsewhere [19][20].

Briefly, chromatographic separation was performed with Xbridge C18 end-capped columns The multiresidue methods used for the 41 other micropollutants are detailed elsewhere [21]. Briefly, the chromatographic column used for separation was a Kinetex XB-C18 Core Shell (100 x 2.1mm, 1.7 µm) equipped with a KrudKatcher (0.2 µm) filter. Vials were kept at ambient temperature until analysis and injection volumes were 100 µL. Column oven temperature was 60°C for both ionization modes. In positive mode, the separation, achieved in 9 min, was performed with a multi-linear gradient with water (acidified with formic acid) and ACN. In negative mode, the separation, achieved in 7 min, was done with a multistep gradient with 0.1 mM ammonium acetate in water and ACN.

The mass spectrometer source was an electrospray in positive mode for 29 molecules:

the UV filter, the hormones (other than estrogens), all the pesticides except 2.4dichlorophenoxyacetic acid and almost all pharmaceuticals except furosemide, ibuprofen and salicylic acid. The source was in negative mode for 12 molecules: all alkylphenols and phenols, 1 pesticide (2.4-dichlorophenoxyacetic acid) and 3 pharmaceuticals (furosemide, ibuprofen and salicylic acid). Acquisitions were performed in scheduled MRM and MRM mode for positive and negative modes, respectively. Two ionization transitions were used for each analyte (except for linear alkylphenols and 2.4-dichlorophenoxyacetic acid for which only one transition was possible) -one for quantification and one for confirmation. The instrumental limits of quantification (direct injection) varied from 1 ng/L for carbendazim to 579 ng/L for 2.4-diclorophenol.

Sampling rate calculation methods

Theoretically, it is possible to model the accumulation of micropollutants in the receiving phase of the POCIS by 3 successive accumulation regimes (as a function of time): a linear (or kinetic/integrative) regime, a pseudolinear regime, and an equilibrium regime [START_REF] Alvarez | Development of an Integrative Sampling Device for Hydrophilic Organic Contaminants in Aquatic Environments[END_REF]. If exchange is isotropic, this accumulation follows a first-order kinetic, which can be described by the equation ( 1):

(

) 1
where Cs is the concentration of a given micropollutant in the sorbent at time t (µg/g), Cw the TWA concentration of the same micropollutant in the water (µg/L), ku the uptake rate constant of the micropollutant on the receiving phase (L/g/d), ke the elimination (or exchange) rate constant of the micropollutant from the receiving phase (d -1 ), and t the time (d).

POCIS is generally used in the linear regime to lead to TWA concentrations. In this regime, the sampler acts as an "infinite sink", and ke is negligible compared to ku. It is therefore possible to simplify the equation ( 1) and to link the concentration quantified in the

) 1 ( t k e u w s e e k k C C   
POCIS to its concentration in the sampling medium via the sampling rate, using equation [START_REF] Alvarez | [END_REF] [2]: [START_REF] Alvarez | [END_REF] where Rs is the sampling rate (L/d), and Ms the mass of sorbent in the POCIS (g).

The frontier between the kinetic regime and the pseudolinear regime corresponds to t1/2, i.e. the time necessary to reach half of the equilibrium concentration [START_REF] Alvarez | Passive Sampling Techniques in Environmental Monitoring[END_REF]. Thus, Rs must be calculated during a period shorter than or equal to t1/2 in order to be accurate. t1/2 is defined as follows:

(3) Rewriting equation ( 2), it is possible to tease out the concentration factor, as indicated in equation ( 4): (4) where CF is the concentration factor (L/g). The concentration factor makes it possible to neutralize the effect of Cw variations.

In this paper, we used equation ( 4) until the t1/2 of each micropollutant in order to calculate Rs. First of all, we drew CF as a function of time (using Cs and Cw quantified at each POCIS removal time). Then, the curve obtained made it possible to determine the ke and thus the t1/2 for each micropollutant using XLStat software. We thus obtained a line whose slope was equal to Rs/Ms. We calculated accurate Rs using this slope multiplied by the mean of POCIS masses exposed until t1/2. The standard deviation of the slope was used to determine the standard deviation of the Rs.

Results and discussion

A reliable calibration system

s s w s M t R C C  e k t 2 ln 2 / 1  s s w s M t R C C CF  
Additional parameters and flow velocities were followed and kept constant during the 42 days experiment duration, as reported in Table 1. Indeed, the RSDs for these parameters never exceeded 27%. During the whole period of the calibration phase, temperature was around 21°C, pH was around 7.6, conductivity was around 430 µS/cm, DOC was around 10 mg/L, and the flow velocities had a mean value of 11 cm/s. Insert Table 1 Figure 2 represents the mean of spiked tap water concentration of the 56 organic micropollutants over the course of 42-day experiment in both aquaria.

Insert Figure 2 Out of the 56 molecules tested, 44 had a mean of spiked tap water concentration close to the nominal value (3  2 µg/L). Among the 12 remaining molecules, 8 are known to degrade in water under our renewal conditions (as proved in a previous experiment), which explains why their concentrations were too low (i.e. n-octylphenol, n-nonylphenol, salicylic acid, fenofibrate, iprodione, thiram with concentrations < 1 µg/L) or dispersed (i.e. tamoxifen: 5.1  3.4 µg/L). The eighth molecule, t-octylphenol, should also be degraded (according to the degradation test) but in this experiment, its concentration was higher than expected (17.0 µg/L). Among the final 4 remaining molecules, 3 had higher mean concentrations than expected (2.4-dichlorophenol: 7.1 µg/L, ofloxacin: 8 µg/L and roxithromycin: 18.5 µg/L) and 1 had a lower mean concentration than expected (resorcinol: 0.4 µg/L). This may be due to biodegradation process for resorcinol [22] and possible matrix effects in water concentrations for t-octylphenol, 2.4-dichlorophenol, ofloxacin and roxithromycin. Either way, ruling out molecules degraded in tap water and resorcinol due of its very low mean water concentration (0.4 µg/L), the relative standard deviation (RSD) never exceeded 47% (t-butylphenol and progesterone) and was lower than 35% for all the other molecules, which is very satisfying considering the long 42-day duration of the calibration experiment.

Rs were not calculated for micropollutants with tap water concentrations far from the nominal value (lower than 1 µg/L or higher than 5 µg/L) or with high RSD (above 35%), except for 2. 4 to RSDs of 46-47%; detailed data shown in supplementary material) for which the calculated CFs lead to well-defined Rs. Nonetheless, for 2.4-dichlorophenol and t-octylphenol, Rs are given for information and still need to be validated.

The supplementary material (S1) reports spiked tap water concentrations for 9 micropollutants (4 with concentrations close to the nominal value: ethinylestradiol, metoprolol, bisphenol A and linuron; 2 with concentrations far from the nominal value: 2.4dichlorophenol and t-octylphenol; 3 with concentration variations higher than 35% over the entire experiment duration: t-butylphenol, progesterone and 4-methylbenzylidene camphor).

Allowing for small variations, our calibration system makes it possible to keep constant additional parameters, flow velocities, and waters concentrations of most of the micropollutants, thus enabling the calculation of well-defined Rs.

Accumulation kinetics

This section discusses the accumulation kinetics curves obtained for the 56 micropollutants for 28 days exposure. These curves enabled us to show the behaviour of each micropollutant in the POCIS receiving phase and, when possible, to estimate t1/2, which is rarely if ever indicated in the literature.

We distinguished 4 different groups:

-group 1, made up of 30 micropollutants showing curvilinear accumulation kinetics as described in the model from Alvarez [START_REF] Alvarez | Development of an Integrative Sampling Device for Hydrophilic Organic Contaminants in Aquatic Environments[END_REF] and equation ( 1 Given that the accumulation of micropollutants from groups 2, 3 and 4 diverged from the theory, it was not possible to determine ke and then t1/2 with XLStat software. For group 2 molecules, we calculated Rs from the triplicate at day 14 and according to equation (4). Figure 3 illustrates the 4 different types of accumulation with two examples from each group.

Detailed accumulation curves for the 56 micropollutants can be found in the supplementary material.

Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058

Table 2 compiles the key information on these micropollutants and indicates their physical-chemical properties, as discussed below.

Insert Figure 3 Insert Table 2 3.2.1. Group 1: micropollutants with a curvilinear accumulation kinetics curve There were 30 micropollutants presenting a curvilinear accumulation kinetics curve and for which the POCIS can supply TWA concentrations as explained in equation ( 2). Group-1 micropollutants had t1/2 from 5 to 693 days. They can be ionized or neutral, with log Kow from 1.34 to 5.12. Their molecular weights vary from 150.1 to 376.7 g/mol.

POCIS are generally exposed in the field for 14 days [START_REF] Alvarez | [END_REF]4,16]. According to table 2, we found a t1/2 higher than or equal to 14 days with neutral micropollutants (20 over 21) and lower than 14 days for ionized micropollutants (8 of 9). Moreover, micropollutants with t1/2 ≥ 14 days have a mean log Kow of 3.5 (±0.8) whereas molecules with t1/2 < 14 days have a mean log Kow of 2.3 (±0.6). Molecular weight did not seem to have any influence on t1/2 duration.

Indeed, molecules with t1/2 ≥ 14 days have a mean molecular weight of 272 g/mol (±52) while molecules with t1/2 < 14 days have a mean molecular weight of 267 g/mol (±68).

The t1/2 calculation is a fairly delicate task since t1/2 can change dramatically with a small variation in a kinetic point, but it remains a valuable criterion for providing the optimal exposure duration of POCIS. TWA water concentrations can be easily calculated from equation (2) for micropollutants with t1/2 ≥ 14 days since they are linearly accumulated during classical in situ 14-day exposure durations. However, for the other group-1 molecules, POCIS should not have to be immersed higher than t1/2 for rigorous TWA concentration calculations (i.e. 5 days for 2.4-DCP).

Lag times (between 3 hours and up to 3 days) were observed for 3.4-dichloroaniline, linuron, 2.4-dichlorophenol, t-butylphenol, prochloraz, megestrol acetate, ethinylestradiol, progesterone, estrone, t-octylphenol and 4-methylbenzylidene camphor. There was no clear explanation for this phenomenon, but lag times were generally (but not systematically) observed for neutral micropollutants or for log Kow higher than 2.3. Lag times have already been reported for prochloraz and 4-methylbenzylidene camphor on a C18 Chemcatcher using PES membranes [21]. These effects had no impact on calculated Rs (except for 2.4dichlorophenol where Rs is possibly underestimated) since t1/2 micropollutants were 11 to 2772 times higher than the lag time duration for t-octylphenol and progesterone, respectively.

3.2.2. Group 2: micropollutants with an inflexion point This group encompassed 13 micropollutants based on their higher accumulation rate in the POCIS during the first week of exposure leading to an inflexion point (generally at day 14) and their coefficients determined as lower than 0.99 compared to the curvilinear model (e.g. ketoprofen, figure 3.d. with R 2 =0.98). For these compounds, XLStat software miscalculated t1/2 since the model was not curvilinear over 28 days. We thus recalculated Rs with POCIS immersed at day 14. We assumed that these Rs were as accurate as those of group 1, since there was an integrative phase after the inflexion point. As was the case for group 1, POCIS can produce TWA concentrations for these micropollutants.

Group-2 micropollutants are generally in ionic form (9 out of 13; Table 2) in a log Kow range of between -0.40 and 3.99. Neutral molecules are relatively polar (log Kow ≤ 2.67). The higher accumulation rate up to 7 days may be explained by a burst effect [START_REF] Alvarez | Development of an Integrative Sampling Device for Hydrophilic Organic Contaminants in Aquatic Environments[END_REF]10]. This phenomenon is due to the time delay required for complete wetting of the POCIS membranes and can be avoided by pre-wetting the POCIS before immersion. These molecules might also accumulate in POCIS following two different sorption mechanisms, e.g. adsorption and then partitioning, or a multi-layer adsorption mirroring gas adsorption on a solid: a first layer directly on the sorbent and a second layer over the first layer [23].

Group 3: micropollutants with random accumulation kinetics curves

The 8 micropollutants forming group 3 were characterized by a random accumulation in the POCIS receiving phase with a concentration factor higher than 3. For these micropollutants, POCIS could not supply reliable TWA concentrations but can be used for screening. We supposed that this type of accumulation is not due to degradation in HLB phase because tests proved that pesticides (acetochlor, alachlor, atrazine, diuron, linuron, 2.4dichlorophenoxyacetic acid), pharmaceuticals (atenolol, carbamazepin, diclofenac, ibuprofen, ketoprofen, metoprolol, naproxen, propranolol, sulfamethoxazole, trimethoprim) and hormones (estrone, estriol, ethinylestradiol) adsorbed on it are well conserved during weeks to months [24][START_REF]Applicabilité de solutions analytiques alternatives pour l'analyse de polluants organiques dans le cadre des programmes de surveillance DCE dans les DOM[END_REF].

These micropollutants are polar or apolar (log Kow from 0.65 to 5.44), neutral or ionized in tap water at pH = 7.6. They include 6 pharmaceuticals, resorcinol, and t-nonylphenol.

These unpredictable accumulations make it impossible to reliably determine t1/2. For all group-3 micropollutants, the RSDs of tap water concentrations were higher than 30%, and 5 molecules (iprodione, t-nonylphenol, salicylic acid, thiram, fenofibrate) are known (from previous experiments) to degrade in tap water under our renewal conditions (degradation time 50 [DT50]: iprodione < 3 d; thiram < 3.5 d; salA< 6 h; feno< 6 h), which may explain some of the random accumulation curves.

Group 4: micropollutants with low or no accumulation kinetics curve

Five molecules were characterized by a very low or no accumulation in the POCIS receiving phase (Table 2). The POCIS is not designed to sample such micropollutants.

As with group 3, these micropollutants can be polar or apolar (log Kow from -0.46 to 6.35), and neutral or ionized in tap water at pH=7.6.

Four of these 5 micropollutants, RSDs of tap water concentrations were higher than 30%, and three (n-octylphenol, n-nonylphenol and tamoxifen, DT50 < 3 h) are known to degrade in tap water under our renewal conditions, which may explain their very low or no accumulation rates.

Accumulation kinetics compared against the literature

There are already literature reports of accumulation kinetics studied in-lab with "pharmaceutical" POCIS for 29 out of our 56 micropollutants of interest. However, the great majority of authors only show linear or curvilinear accumulation curves, notable exceptions being MacLeod et al. [7] and Harman et al. [13]. We cannot find any author taking time to discuss atypical accumulation (with inflexion point, random or low) and the consequences on Rs calculation. For comparison, Table 3 reports kinetic curves data studied in the literature.

Insert Table 3

Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058

The 17 group-1 micropollutants studied in the literature always showed linear accumulation, thus confirming our findings. Nevertheless, some of these micropollutants were exposed for only 8 days (t-butylphenol, metoprolol, propranolol and linuron) and their accumulations were drawn with only 4 samples, which is not enough to correctly estimate the t1/2. It is important to have a lot of datapoints, especially at the beginning of the calibration, to be able to refine the line of the kinetics accumulation. MacLeod et al. [7], Harman et al. [13] and Martinez-Bueno et al. [16] reported nonlinear accumulation curves for a few micropollutants (from groups 2, 3 and 4, and including antibiotics and some alkylphenols) but did not discuss potential explanations for these atypical accumulation patterns nor the consequence on Rs calculation. Finally, contrary to our results, MacLeod et al. [7], Li et al. [14] and Bartelt-Hunt et al. [11] all found linear kinetic accumulations for anti-inflammatories and betablockers. This divergence may be explained by their different calculation methods (measuring decreasing concentrations in water instead of increasing concentrations in POCIS) and calibration systems (beaker or bottle and distilled water).

Sampling rates (R s )

One essential point for accurately calculating Rs is to be in the kinetic regime of the POCIS. Thus, for group-1 molecules, we calculated Rs using the slope of the line CF=f(t)

until their respective t1/2, as explained in the experimental section. For group-2 molecules, we calculated sampling rates at day 14 using equation [4], with a possible bias since the duration of the kinetic regime was not well-defined in this case. Note that it was not possible to calculate any sampling rates for group-3 or group-4 molecules because they were randomly, poorly or not at all accumulated.

We compiled calculated Rs in Table 4 and compared them against Rs values reported in the literature when obtained with the same kind of POCIS ("pharmaceutical" POCIS with 45.8 cm 2 surface and 200 mg of receiving phase) and in the same conditions (under agitation and at between 15 and 25°C). Sampling rates varied from 0.025 L/d for atenolol up to 0.398 for t-butylphenol. Our study produced 16 laboratory Rs now published for the first time here.

Insert Table 4 The POCIS is a useful tool for sampling herbicides, hormones, some alkylphenols, some pharmaceuticals like benzodiazepines, and the UV filter. Indeed, for these micropollutants (group 1), the kinetic regime is equal to or higher than 14 days, which is a comfortable duration for using the POCIS in situ to determine TWA concentrations. If the POCIS is immersed just for 7 days, it can be useful for betablockers with log Kow > 1.34 (t1/2 between 7 and 14 days; group 1). We assume that POCIS are suitable for some antibiotics, antiinflammatories, and hydrophilic betablockers with Rs calculated from the triplicate at 14 days (group 2). For group-3 micropollutants, the POCIS is only suitable for screening. For group-4 molecules, the POCIS is simply not suitable.

Compared to the Rs from authors using the same kind of calibration system (i.e. aquaria with a flow velocity arriving directly at the front of the POCIS; Mazzella et al. [10] and Lissalde et al. [15]), our Rs values are generally less than 2-fold different. Our Rs were also close (i.e. less than 2 fold-different) to reported values for hydrophobic molecules (log Kow > 2.65), except Li et al. [14] who obtained significantly higher Rs than ours (or than those reported in the literature), including up to 11-fold higher values for sulfamethoxazole. As stated earlier, this can be explained by the different calculation method and calibration system used, which further underlines the critical need to define standardized protocols to obtain comparable sampling rates [START_REF] Morin | [END_REF].

Is it possible to predict R s from micropollutant physical-chemical properties?

We found a quadratic correlation between Rs and log D for betablockers, herbicides and hormones, as illustrated in Figure 4. We used this parameter because it takes into account the hydrophobic-hydrophilic character (log Kow) of a micropollutant as well as its potential charge (pKa). Other studies have also attempted to find correlations to explain Rs values, but with log Kow and not log D [8,10,[START_REF] Togola | [END_REF].

Insert Figure 4 Figure 4 shows increasing Rs values as a function of log D for betablockers, herbicides and hormones. However, the line of the curve is not the same for a given family, and reached a plateau for the betablockers (log D 0.0-0.5), herbicides (log D 2.5-3.5) and hormones (log D

Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058 3.5-4.5) families. The range of Rs can be predicted as a function of log D for some but not all micropollutants, which suggests that other physical-chemical properties also need to be considered.

Conclusion

We report a calibration experiment that is reliable and robust in terms of constant values for micropollutant concentrations in tap water, flow velocities and additional parameters (temperature, pH, conductivity, dissolved organic carbon concentration). This system allowed us to study the accumulation of 56 organic micropollutants using the "pharmaceutical" POCIS for up to 28-day exposure periods. We distinguished The POCIS is particularly suitable for sampling neutral micropollutants (included in group 1) with log Kow ranging from 2.5 to 5, such as hormones, pesticides or several pharmaceuticals. Indeed, the kinetic regime for this type of molecule is higher than or equal to 14 days, which is suitable for in situ application of POCIS to evaluate TWA water concentrations. POCIS can also produce TWA concentrations for more hydrophilic (with log Kow as low as -0.34) and ionized micropollutants such as antibiotics, anti-inflammatories and betablockers (included in groups 1 and 2). However, POCIS is only suitable for screening for micropollutants with random accumulation (e.g. thiram, roxithromycin, group 3), and is not at all suitable for micropollutants with very low or no accumulation (e.g. metronidazole, tamoxifen, group 4).

To our knowledge, this is the first paper dealing with POCIS-derived accumulation curves with an inflexion point. There is a need to determine a model which better described this type of accumulation. Moreover, it would be interesting to better understand the underlying processes involved in POCIS accumulation of these micropollutants (large burst effect, two biphasic accumulation phenomena, multi-layer adsorption are candidates).

Analysis of the POCIS membranes could give clues.

Finally, it is difficult to predict Rs as a function of the physical-chemical properties of target molecules, except for betablockers, herbicides and hormones with log D. This point should be a direction for further research.

Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058

Table 1

Mean values for temperature, pH, conductivity, DOC and flow velocity in the two aquaria, and their associated variability (RSD). Atenolol (Group 2) Atenolol (Group 2) Roxithromycin (Group 3) Roxithromycin (Group 3) 674 Fig. 3. The 4 types of micropollutant accumulation in the POCIS receiving phase, illustrated 675 by a: propranolol (group 1), b: diclofenac (group 1), c: atenolol (group 2), d: ketoprofen 676 (group 2), e: salicylic acid (group 3), f: roxithromycin (group 3), g: paracetamol (group 4), 677 h: tamoxifen (group 4). 678

0 0,5 1 1,5 2 2,5 3 3,5 4 4,
0 0,5 1 1,5 2 2,5 3 3,5 4 4,
0 0,5 1 1,5 2 2,5 3 3,5 4 4,
0 0,5 1 1,5 2 2,5 3 3,5 4 4,

Table 2 680

Characteristics of the 56 micropollutants. Micropollutants are classified according to type of 681 accumulation curve (groups 1-4) and by increasing t1/2 followed by increasing log Kow. 682 [11] j a : Flow-through, aquarium (tap water, 50 L, 3 µg/L, 21°C, 10 cm/s), POCIS analysis at t = 1, 3, 6, 12 h and 1, 3, : not calculated because randomly or poorly accumulated in the POCIS (i.e. qualifying as group-3 or group-4 molecules).

Group

b : never determined with this POCIS configuration and in these conditions.

Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058 y = -0.

  [t-BP], n-nonylphenol [n-NP], t-nonylphenol [t-NP], n-octylphenol [n-OP], t-octylphenol [t-OP], resorcinol [Res]), 27 pharmaceuticals including 5 antibiotics (metronidazole [Metro], ofloxacin [Oflo], roxithromycin [Roxi], sulfamethoxazole [Sulfa], trimethoprim [Trim]), 5 anti-inflammatories (diclofenac sodium salt [Diclof], ibuprofen [Ibu], ketoprofen [Keto], naproxen [Napro], salicylic acid [SalA]), 2 benzodiazepines (lorazepam [Lora], oxazepam [Oxa]), 10 betablockers (acebutolol hydrochloride [Ace], atenolol [Ate], betaxolol [Bet], bisoprolol fumarate [Bis], metoprolol tartrate [Met], nadolol [Nad], oxprenolol [Oxp], propranolol hydrochloride [Prop], sotalol hydrochloride [Sot], timolol hydrogen maleate [Tim]), 2 lipopenics (bezafibrate [Beza], fenofibrate [Feno]) and 3 other pharmaceuticals (carbamazepine [Carba], furosemide [Furo], paracetamol [Para]), 11 pesticides including 4 fungicides (carbendazim [Carb], iprodione [Ipr], prochloraz [Pro], thiram [Thi]) and 7 herbicides (2.4-dichlorophenoxyacetic acid [2.4-D], 3.4-dichloroaniline [3.4-D], acetochlore [Acet], alachlore [Ala], atrazine [Atra], diuron [Diu], linuron [Lin]], 10 hormones including 5 estrogens (estrone [E1], 17-estradiol [-E2], 17-estradiol [-E2], estriol [E3], ethinylestradiol [EE2]), 2 progestogens (megestrol acetate [MegA], progesterone [P]), 1 androgen (testosterone [T]) and 1 anticancer drugs (tamoxifen [Tamo]) and 1 UV filter (4methylbenzylidene camphor [4-MBC]). One micropollutant (2.4-dichlorophenol-d3 [2.4-DCP]) and one internal standard (17β-estradiol acetate) were purchased from CIL (Sainte-Foy-La-Grande, France). The internal standard for betablockers (metoprolol impurity A) was purchased from LGC (Molsheim, France). The POCIS was built using Oasis® HLB bulk sorbent (average particle diameter: 60 µm) and hydrophilic polyethersulfone (PES) SUPOR 100 membrane disc filters (0.1 µm, 90 mm membrane diameter) purchased from Waters (Guyancourt, France) and Pall (Saint-Germain-en-Laye, France), respectively. Empty glass solid phase extraction (SPE) tubes (6 mL) and polytetrafluoroethylene (PTFE) frits (20 µm porosity) were purchased from Sodipro (Echirolles, France). The calibration system was composed of a Harvard Type 22 syringe pump from Harvard Apparatus (Les Ulis, France) and a Ismatec model Ecoline VC-MS/CA8-6 peristaltic pump from Thermofisher (Illkirch, France). The chromatographic separation of 10 betablockers and 5 estrogens ([E1], [-E2], [-E2], [E3], [EE2]) was performed with Xbridge C18 end-capped columns (150 x 2.1 mm, 3.5 µm) from Waters (Guyancourt, France) equipped with guard columns. The separations of the 41 remaining molecules were performed with a Kinetex XB-C18 Core Shell (100 x 2.1 mm, 1.7 µm) equipped with a KrudKatcher (0.2 µm) filter from Phenomenex (Le Pecq, France).

(150 x 2 .

 2 1 mm, 3.5 µm) equipped with guard columns. Vials were kept at 4°C during analysis. Injected volumes were 10 µL. Column oven temperature was set at 28°C for betablockers and 30°C for estrogens. Gradients with LC-MS-grade water (buffered with ammonium formiate for betablockers) and acetonitrile were applied at a flow rate of 0.2 mL/min: from 1% ACN at 0 to 5 min ramped up to 100% ACN at 21 min until 29 min for betablockers, and 40% ACN from 0 to 2 min ramped up to 80% ACN at 4.5 min until 7 min and up to 100% ACN at 8.25 min until 15 min for estrogens. Separations were achieved in less than 20 min and 12 min for betablockers and estrogens, respectively.Ionization was performed with an electrospray source in positive mode for betablockers and negative mode for estrogens. Acquisitions were performed in multiple reaction monitoring (MRM) mode. Detection included 2 ionization transitions for each analyte -one for quantification and the other for confirmation. The instrumental limits of quantification (direct injection) were 100 ng/L for betablockers and 150-700 ng/L for estrogens depending on analyte micropollutant.

  ), -group 2, made up of 13 micropollutants having an inflexion point in their accumulation kinetics curve, -group 3: made up of 8 micropollutants with random accumulation kinetics curves, -group 4: made up of 5 micropollutants, characterized by very low (CF max = 3 L/g) or inexistent accumulation.

Fig. 1 .

 1 Fig. 1. Schematized flow-through calibration system for POCIS exposure.

Fig. 2 .Propranolol

 2 Fig. 2. Mean spiked tap water concentrations of the 56 studied micropollutants (n=74 for betablockers and estrogens i.e. E1, α-E2, β-E2, E3, EE2; n=24 for others) across both aquaria. Micropollutants are grouped by family and by increasing log Kow. The bold dashed line represents the nominal value. Asterisks flag molecules degraded in tap water with 35% renewal per day (based on data from previous experiments).

7, 14 ,

 14 21, 28 d, kinetic accumulation drawn by measurement in the POCIS receiving phase b : Flow-through, aquarium (seawater, 200 L, 0.050-0.120 µg/L, 10°C, 100 rpm), POCIS analysis at t = 7, 14, 21, 28 d, kinetic accumulation drawn by measurement in the POCIS receiving phase c : Static renewal, beaker (distilled water, 1 L, 0.5 µg/L, 23.5°C, 350 rpm), POCIS analysis at t = 7, 14, 28 d, kinetic accumulation drawn by measurement in the POCIS receiving phase d : Flow-through, aquarium (distilled water, 30 L, 0.01 to 1 µg/L, 15°C, ? cm/s), POCIS analysis at t = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 d, kinetic accumulation drawn by measurement in the POCIS receiving phase e : Static, aquarium (tap water with 2 µM CuSO 4 , 80 L, 1-2 µg/L, 17°C, 2-3 cm/s), POCIS analysis at t = 5, 10, 15, 21 d, kinetic accumulation drawn by measurement in the POCIS receiving phase f : Static renewal, aquarium (tap water, 80 L, 1 µg/L, 17°C, 2-3 cm/s), POCIS analysis at t = 6, 12, 18, 24 d, kinetic accumulation drawn by measurement in the POCIS receiving phase g : Static renewal, beaker (seawater, 2 L, 0.5 µg/L, 21°C, ? rpm), POCIS analysis at t = 1, 3, 7 d, kinetic accumulation draw by measurement in the POCIS receiving phase h : Static renewal, beaker (distilled water, 3 L, 1 µg/L, 28°C, 12 cm/s), POCIS analysis at t = ? (total exposure duration 25 d), kinetic accumulation drawn by measurement decreases in distilled water

Fig. 4 .

 4 Fig. 4. Sampling rate (Rs) versus log D for a) betablockers, herbicides and hormones, b) betablockers only, c) herbicides only, d) hormones only.

  -dichlorophenol and t-octylphenol (water concentrations of 7.1 µg/L and 17.0 µ/L respectively; detailed data shown in supplementary material), and for t-butylphenol, progesterone, 4-methylbenzylidene camphor (random water concentration variations leading Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058

Table 3

 3 Comparison of kinetic accumulation curves for "pharmaceutical" POCIS. Molecules are grouped by family and by increasing log Kow. Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058 Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058

	Micropollutant	t 1/2 (day)	Log K ow a	Log D (pH=7.6)	pKa a	Ionization (pH=7.6)	Molar mass a (g/mol)

Table 4

 4 Author-produced version of the article published in Talanta(2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058 Static, bottle (distilled water, 3 L, 2-10 µg/L, 25°C, 800-900 rpm), POCIS analysis at t = ? (total exposure duration 8 d), kinetic accumulation drawn by measurement decreases in distilled water Static, beaker (distilled water, 2 L, 5 µg/L, 25°C, 450 cm/s), POCIS analysis at t = 28 d, kinetic accumulation drawn by measurement decreases in distilled water Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058Sampling rates (Rs) calculated for the 56 studied micropollutants and comparison against literature data (micropollutants grouped by families and by increasing log Kow). Comparison performed only when studies used the same POCIS configuration and the same calibration conditions as here. Micropollutants in bold characters correspond to unpublished literature Rs.

	Molecule (group)	Family	Log Kow	Rs (L/d)	Rs from literature (L/d)	Difference (%)	Reference
	Res (3)		1.37	a	b
	2.4-DCP (1)		2.88	0.068 (±0.005)	b
	t-BP (1)		3.21	0.398 (±0.044)	0.120 0.170	-70 -57	[27] [13]
	BPA (1)	Alkylphenols and	4.04	0.245 (±0.006)	0.117 (±0.019) 0.835 (±0.058)	-52 240	[8] [14]
	t-OP (1)	phenols	4.69	0.065 (±0.005) 0.1204 (±0.0110)	85	[8]
	t-NP (3)		5.44	a	b
	n-OP (4)		5.35	a	0.010 (±0.008)		[8]
	n-NP (4)		5.74	a	0.117 (±0.012) 2.459 (±0.131)		[8] [14]
	Metro (4)		-0.46	a	b
	Oflo (3)		0.65	a	b
	Sulfa (2)	Antibiotics	0.79	0.030 (±0.003)	0.339 (±0.057) 0.118 (±0.012)	1015 288	[14] [11]
	Trim (2)		1.28	0.162 (±0.014)	0.436 (±0.006) 0.360 (±0.210)	169 122	[14] [7]
	Roxi (3)		3.00	a	0.723 (±0.430)		[7]
	SalA (3)		1.98	a	b
	Napro (2) Keto (2)	Anti-inflammatories	2.99 3.61	0.084 (±0.011) 0.118 (±0.007)	0.392 (±0.024) 0.116 (±0.053) 0.135 (±0.035)	368 38 11	[14] [7] [7]
	Ibu (2)		3.84	0.118 (±0.006)	0.348 (±0.052) 0.400 (±0.008)	181 223	[14] [11]
	Diclof (1)		4.26	0.225 (±0.009)	0.166 (±0.052) 0.170	-26 -24	[7] [26]
	Oxa (1)	Benzodiazepines	2.92	0.226 (±0.009)	b
	Lora (1)		3.53	0.205 (±0.006)	b
	Sot (2)		-0.40	0.036 (±0.008)	0.151 (±0.021)	386	[14]
	Ate (2)		0.43	0.025 (±0.005)	0.094 (±0.015) 0.040 (±0.070)	331 84	[14] [7]
	Nad (2)	Betablockers	0.87	0.114 (±0.009)	0.447 (±0.036)	299	[14]
	Tim (1)		1.34	0.210 (±0.012)	b
	Ace (1)		1.53	0.166 (±0.008)	b
	Met (1)		1.76	0.195 (±0.012)	0.465 (±0.039) 0.599 (±0.270)	138 206	[14] [7]

i : j : b a

Author-produced version of the article published in Talanta (2013), vol.109, p. 61-73 The original publication is available at http://www.sciencedirect.com/ doi : 10.1016/j.talanta.2013.01.058

Acknowledgements

The authors thank the Cluster de Recherche Rhône-Alpes Environnement for financing Nicolas Morin's thesis and the MEEDM (Ministère de l'Ecologie, de l'Environnement, du Développement durable et de la Mer) for financing Julien Camilleri's thesis. We also thank Ghislaine Grisot, Julie Iaciancio, Loïc Richard and Hélène Sanejouand for their valuable support with analysis, and ATT for English language editing.

Author-produced version of the article published in Talanta (2013)