P Hamon

L Ercolei

Performance of a biomass adapted to Oncological Ward Wastewater vs. biomass from municipal WWTP on the removal of pharmaceutical molecules

Keywords: Pharmaceutical compounds, Acclimated sludge, pharmaceutical removal, sorption, biotransformation

The performance of a biomass adapted to Oncological Ward Wastewater (OWW) in a membrane bioreactor (MBR) was compared with that of a municipal WWTP, on the removal of pharmaceutical molecules and more specifically on their overall resistance and purifying ability in the presence of pharmaceutical cocktails. Sorption and biotransformation mechanisms on two antineoplastics, one antibiotic and a painkiller were evaluated. Sludge acclimated to OWW allowed for a 34% increase in the removal rate and in the minimum inhibition concentration. The percentage of the amounts of specific pharmaceutical compounds removed by biotransformation or by sorption were measured. These results are positive, as they show that the observed removal of

Introduction

The removal of pharmaceutical residues in wastewater treatment plants (WWTP) by activated sludge is carried out through two mechanisms: biotransformation (biological removal and metabolization of the parent molecule) and sorption; photo transformation and air-stripping are negligible [START_REF] Poseidon | Assessment of Technologies for the Removal of Pharmaceuticals and Personal Care Products in Sewage and Drinking Water Facilities to Improve the Indirect Potable Water Reuse[END_REF]. The biotransformation of pharmaceutical compounds follows a pseudo-first order model [START_REF] Joss | Biological degradation of pharmaceuticals in municipal wastewater treatment: Proposing a classification scheme[END_REF] in a concentration range which does not inhibit biomass. The Hydraulic Retention Time (HRT) may therefore be optimized according to concentrations at process input and to the value of the ki,biol constant of the pharmaceutical molecule. So the ki,biol constant depends on the degradability of the compound but also on the composition of the sludge, which influences the mechanism of biodegradation of pharmaceutical compounds in several ways. [START_REF] Joss | Biological degradation of pharmaceuticals in municipal wastewater treatment: Proposing a classification scheme[END_REF] classified pharmaceutical compounds into 3 groups, according to their constants (i) ki,biol < 0,1 L.gTSS -1 .d -1 : no significant transformation/removal through biodegradation; (ii) 0,1 < ki,biol < 10 L.gTSS -1 .d -1 : partial removal (20 % to 90 %) and (iii) ki,biol > 10 L.gTSS -1 .d -1 : more than 90% transformation/removal via biodegradation. Their results show that only 4 out of the 35 pharmaceuticals molecules studied (estrone, estradiol, ibuprofen and paracetamol) could be removed by 90% through biotransformation but that this mechanism could be overestimated for a third of the compounds studied. It does not seem possible to conclude on the bio transformability of a pharmaceutical molecule because of the few exceptions that were obtained for antibiotics and anti-inflammatory agents. So the ki,biol constant must be determined experimentally. pH, redox potential, stereochemical structure and the chemical structure of the sorbent and of the sorbed molecule may influence the effect of the sorption mechanism on the activated sludge [START_REF] Kümmerer | The presence of pharmaceuticals in the environment due to human usepresent knowledge and future challenges[END_REF], be it through adsorption or absorption. Thus the influence of pH on the removal of ionizable micropollutants in a Membrane Bioreactor (MBR) was confirmed by applying an acid pH which modified the hydrophobicity of some compounds which are not inclined to sorption on the bacterial flocs at a neutral pH [START_REF] Urase | Factors affecting removal of pharmaceutical substances and estrogens in membrane separation bioreactors[END_REF][START_REF] Tadkaew | Effect of mixed liquor pH on the removal of trace organic contaminants in a membrane bioreactor[END_REF]. The solid-water partition coefficient ܭ , also called Nernst coefficient, was then introduced as the most appropriated parameter representing the sorbed fraction of a molecule on suspended matter [START_REF] Schwarzenbach | des procédés d'épuration biologique : application aux molécules anticancéreuses traitées par bioréacteur à membrane[END_REF]Ternes et al., 2004). The sorption of a compound is considered negligible for municipal WWTP if ܭ is smaller than 500 L.kgTSS -1 as it would represent less than 10% removal (Ternes et al., 2004). [START_REF] Joss | Removal of pharmaceuticals and fragrances in biological wastewater treatment[END_REF] give a lower threshold value at 300 L.kgTSS -1 , before taking the sorption mechanism into account. [START_REF] Sipma | Comparison of removal of pharmaceuticals in MBR and activated sludge systems[END_REF] conclude that the sorption of pharmaceutical compounds on activated sludge is generally a minor removal mechanism, due to the low values of ܭ in pharmaceuticals. Numerous pharmaceutical molecules are hydrophilic, which a priori limits sorption phenomena. Nevertheless, very hydrophilic molecules, such as antibiotics from the fluoroquinolone class, are removed very efficiently through sorption due to electrostatic interactions (Göbel et al., 2007;[START_REF] Vieno | Elimination of pharmaceuticals in sewage treatment plants in Finland[END_REF]. Out of 40 micropollutants that were studied in an MBR, the 14 very hydrophobic molecules were all removed at more than 85% [START_REF] Tadkaew | A rapid method to measure the solid-water distribution coefficient (Kd) for pharmaceuticals and musk fragrances in sewage sludge[END_REF]. It is necessary to distinguish between the 2 mechanisms of pharmaceutical micropollutants removal in order to estimate the proportion transferred to the sludge, which would allow for an assessment of the environmental relevance of the removal procedure / disposal of excess WWTP sludge.

Moreover, the treatment process may influence the ability of the biomass to resist toxic charges, as [START_REF] Henriques | The impact of floc size on respiration inhibition by soluble toxicants-a comparative investigation[END_REF][START_REF] Henriques | The role of extracellular polymeric substances in the toxicity response of activated sludge bacteria to chemical toxins[END_REF] state that some processes are more sensitive to inhibition: it is the case of activated sludge flocs, which boost the formation of small aggregates (such as MBR) and processes involving a high shear. In their study,

M A N U S C R I P T A C C E P T E D

ACCEPTED MANUSCRIPT 5 respirometric tests on the biomasses of 2 MBR revealed an inhibition 1.25 and two times greater than that of a conventional activated sludge process while in contact with chemical toxins, with MBR bacterial flocs smaller by 41%.

The choice of treatment process configuration is very important in its ability to resist the presence of toxic material, as shear stress rate is different according to configuration. It is therefore expected that the MBR configuration may influence the ability of activated sludge to resist and to acclimate to a highly concentrated pharmaceuticals effluent. For this study, we decided to compare the performance of a biomass adapted to Oncological Ward Wastewater (OWW) with that of a municipal WWTP, on the removal of pharmaceutical molecules and more specifically on their overall resistance to the presence of pharmaceutical cocktails and the preservation of their purifying ability.

Removal of one of the oncological ward's most consumed antineoplastics (5-FU) was quantified for both biomasses. Then removal of easily biodegradable substrate in the presence of pharmaceutical cocktails (antineoplastics and antibiotics) was measured for both biomasses in order to determine whether (i) adaptation to OWW permitted to increase resistance of the biomass to pharmaceuticals and whether (ii) one class of pharmaceuticals is more harmful than another to the performance of both biomasses. This objective arose from the different uses of antineoplastics and antibiotics in hospitals. While antineoplastics and antibiotics are administered continuously in oncologic wards, the antibiotics are given to prevent possible post-surgical infections and their concentrations in effluent can be strongly modified as a function of time and the number of patients. Hence it is assumed that the adaptation of the biomass to antibiotics is made all the more delicate by the occasional presence of concentration peaks in hospital effluents (OWW). Finally sorption and biotransformation mechanisms

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 6
on two antineoplastics, one antibiotic and a painkiller were studied for both activated sludge.

II.

Equipment and methods

II.1. MBR and hospital effluents

A pilot-scale membrane bioreactor was designed, built and set up underneath the oncological ward of the Timone hospital (Marseille, France). The MBR pilot was designed for treating 1 to 2 L.h -1 of hospital effluent from the oncological ward. The pilot has a maximum capacity of 60 L, with an operating volume set at 32 L. A 3.1 kW refrigeration unit allowed for regulation of activated sludge temperature at 25 ± 2°C. OWW were kept in a storage tank with a maximum capacity of 200 L and were renewed every other day. campaigns to OWW were carried out: one using an external membrane bioreactor (eMBR) and another using an external submerged membrane bioreactor (sMBRe). The biomasses from the eMBR and sMBRe were acclimated to effluents from the Timone oncological ward (Marseille) for more than 150 days each [START_REF] Hamon | Determination of sorption properties of micropollutants: What is the most suitable activated sludge inhibition technique to preserve the biomass structure?[END_REF]. The pipe collected wastewater from 6 rooms without dilution by the ward's other activities.

Pretreatment consisted of maceration with a Saniflo (Plus Silence, SFA) and 0.5 mm cut off filtration. After a few days of operation sampling of the OWW was carried out at night in order to avoid dilution by shower drain water, thus an effluent with a higher ammonium content was obtained. In spite of the standardization of the sampling method, large fluctuations in COD and N-NH4 + concentrations were measured. COD and N-NH4 + concentrations were stabilized by feeding the MBR pilot with half OWW and half synthetic substrate during the second half of the first acclimation campaign in an eMBR configuration. Composition of the synthetic substrate which allowed for dilution of the polluting charge specific to OWW was determined using the average COD (800 mg.L -1 as sugar C6H12O6) and N-NH4 + (31 mg.L -1 as (NH4)2SO4) concentrations, which were measured over a two-month period. Concentrations in mineral salts were set according to literature (Han et al., 2005 ;Barrioz-Martinez, 2006): C/N/P ratio of the synthetic effluent was 100/4/2. In both MBR configurations the retentate was returned to the nitrification tank while the permeate was sent back to the oncological ward waste water pipe. The features of both MBR, of the acclimation parameters and of the activated sludge are listed in table 1. The COFRAC accreditation certifies the technical competence of testing and calibration laboratories to perform specific tasks. The procedure for dosing 5-FU in blood plasma was successfully applied to OWW and treated water. 5-FU was analyzed with HPLC-UV (254 nm). The limit of quantification was 5 µg.L -1 . The detection limits were obtained with several injections of compounds from 1 to 10 µg.L -1 . An accurate detection and repeatability were obtained from the concentration at 5 µg.L -1 . In detail, the analytical system was divided into three parts: (A) a mobile phase composed of 0.05 M monopotassium phosphate (KH2PO4) was adjusted to pH 3 with orthophosphoric acid and filtration at 0.2μm. (B) 500μL of the sample to be analyzed was acidified with 20μL of 5% orthophosphoric acid; ibromouracil (50mL, 10μg.mL -1 ) was added. The sample was extracted with 6 mL of n-propanol/diethylether 10:90 (v:v), mixed with an automatic vortex for 10 minutes before centrifugation for 15 minutes at 3000 g at 4°C.

II.2 Compounds and analysis

M

The organic phase was sampled and evaporated in a water bath under nitrogen. The dry residue was recovered in 100 μL of mobile phase and was centrifuged for 4 minutes. (C)

The samples were analyzed by HPLC-UV: 7 solvent samples were injected for calibration (the 7 samples covered the whole concentration range), then the samples to be analyzed were injected, and finally 3 samples were injected for quality control. The four

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT
remaining molecules (cyclophosphamide, ifosfamide, sulfamethoxazole and codeine)

were analyzed simultaneously by liquid chromatography combined with tandem mass spectrometry (LC/MS-MS). Detection limits were first estimated by calculation by injecting a low-concentration standard solution: the limit of detection is at least equal to 3 times the background noise and the limit of quantification is, to a minimum, equal to 10 times the signal of the background noise. The quantification limits were controlled by injecting a standard solution at this given concentration. Then real samples were doped at this given concentration in order to assess the accuracy and reliability of the analytical method. The limit of quantification of the method was 2.5 µg.L -1 . The LC/MS-MS was calibrated with a 200mg.L -1 solution of our molecules of interest in methanol. A calibration range of 0, 1, 2.5, 10, 30, 50 and 100 µg.L -1 in ultrapure water / methanol (80/20) was obtained by diluting the deuterated internal standards to 30 µg.L -1 (sulfamethoxazole-D4, diclofenac-D4, ketoprofenD4 and caffeine). In detail, the analytical system used was composed of an AGILENT 1100 HPLC equipped with a high pressure pump, an automatic injector (thermostated by Peltier effect) and a SCIEX, API400 tandem mass spectrometer. Quantification was carried out with a calibration in solutions containing internal standards. The analytical conditions were: (i) an analytical column: ZORBAX Eclipse Plus C18 (100mm x 2.1mm x 3.5μm) with guard pre-column ZORBAX Eclipse Plus C18 (5 μm x 12.5 mm). (ii) The solvent gradient parameters were set through two channels: channel A: ultra-pure water with 0.1% formic acid and

II.3 Pharmaceutical cocktails

The influence of various pharmaceutical cocktails on the performance of both biomasses (acclimated to OWW and municipal WWTP) was assessed by using the kinetics of degradation of easily degradable substrates (COD, NH4 + ) in a batch reactor. The pharmaceutical cocktails were prepared using hospital pharmaceuticals. The composition of the antineoplastics cocktail was based on the maximum concentration of 5-FU found in OWW during the acclimation period of the biomass, i.e. 1287 µg.L -1 , on the metabolization rate of each pharmaceutical and on the maximum quantity consumed in the oncological ward unit to which the MBR was connected. The concentrations thus calculated are shown in Table 2. ሾ5 -ܷܨሿ ௫ : Maximum concentration in 5-FU detected in OWW during the first experimental campaign (µg.L -1 ) npharmaceutical: annual consumption of the pharmaceutical in the unit (mg.year -1 ) n5-FU: annual 5-FU consumption in the unit (mg.year -1 )

1-τpharmaceutical: non-metabolized pharmaceutical rate (-)

1-τ5-FU: non-metabolized 5-FU rate (-)
The concentrations of the antibiotics cocktail were defined arbitrarily in order to obtain a total concentration in the same order of magnitude as that of the antineoplastics. Thus the concentration of each of the ward's four most consumed antibiotics was set at 1 mg.L -1 (Table 2).

It should be noted that these cocktails do not in any way represent the average or the maximum concentrations that could be detected in OWW. Five original COD concentrations plotted to the quantity of TSS were tested for each "type of biomasspharmaceutical cocktail" pair: 0.1 -0.2 -0.5 -1 -3 gCOD.gTSS -1 . Total duration of the tests was 4h. However, the duration that was used to calculate the maximum degradation velocity varied according to original concentrations and corresponded to the linear degradation velocity of the COD.

II.4 Sorption and biotransformation tests

The technique to inhibit activated sludge must be adapted to sorption tests: it must allow for complete inhibition of the biotransformation mechanism and for preservation

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 12
of the structural integrity of the activated sludge. Prior to sorption text, it was necessary to determine the most suitable activated sludge inhibition technique for carrying out these tests in order to ensure the consistency of batch tests with sorption phenomenon in the MBR: gas purging is the only technique that does not affect the structure of the biomass [START_REF] Hamon | Determination of sorption properties of micropollutants: What is the most suitable activated sludge inhibition technique to preserve the biomass structure?[END_REF]. Moreover, this inhibition is very easily implemented and the inhibition state is reached immediately, as the concentration in dissolved oxygen drops down almost instantaneously to zero. The quantity of the selected pharmaceuticals, which were removed thanks to the sorption mechanism was evaluated for the activated sludge from the sMBRe pilot and from the municipal WWTP of Rousset (France). The sorption tests were carried out over 4h as this duration allows ifosfamide and cyclophosphamide to reach sorption equilibrium, whatever the origin of the activated sludge (Seira, 2013). Sorption equilibrium of sulfamethoxazole was reached in 2h [START_REF] Yang | Sorption and biodegradation of sulfonamide antibiotics by activated sludge: Experimental assessment using batch data obtained under aerobic conditions[END_REF]. There is no available data concerning codeine. Activated sludge filtered with coffee filters (average pore size 100 µm) was re-suspended with distilled water, so as not to use the supernatant from the sMBRe pilot, as it was likely to contain the selected pharmaceuticals at considerable concentrations, which could distort results.

Activated sludge concentration in TSS was brought down to 4 g.L -1 using coffee filters.

Sorption tests were performed on pharmaceutical cocktails in 4 different original concentrations close to 100 -250 -500 and 1000 µg.L -1 . The original measured concentrations were slightly different but in the correct order of magnitude. Thus, to allow for easier reading, results are presented according to that concentration factor (1 -2.5 -5 and 10). Reductions and KD values were calculated using actual original concentrations. The activated sludge was placed in anaerobic conditions. Water was deoxygenated with dinitrogen, the pharmaceuticals were introduced and the initial

III. Results

III.1. Removal Performance

III.1.1. 5-FU

During the acclimation phase, 5-FU was almost consistently detected in OWW at concentrations up to 1287 µg.L -1 (minimum 49.6 µg.L -1 / average 440 µg.L -1 / 150 days).

These concentrations are very high compared with those measured in previous research: between 11.5 and 122 µg.L -1 for [START_REF] Mahnik | Fate of 5-fluorouracil, doxorubicin, epirubicin, and daunorubicin in hospital wastewater and their elimination by activated sludge and treatment in a membrane-bio-reactor system[END_REF] and between 35 and 92 ng.L -1 for [START_REF] Kosjek | Fluorouracil in the environment: Analysis, occurrence, degradation and transformation[END_REF]. The permeate samples which were analyzed show good removal of 5-FU by acclimated activated sludge, as reductions are above 90% in spite of high initial concentrations, sometimes greater than 1 mg.L -1 . Specific degradation velocity seems relatively proportional to the 5-FU initial concentration (pseudo-first order) (Figure 1). If the VspecificMBR > LOQ and VspecificMBR < LOQ, the velocity was calculated from the value of the measured concentration and from the value of the LOQ

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 14
respectively. Removal of 5-FU was assessed during similar research on treatment of OWW in an Austrian hospital using MBR [START_REF] Mahnik | Fate of 5-fluorouracil, doxorubicin, epirubicin, and daunorubicin in hospital wastewater and their elimination by activated sludge and treatment in a membrane-bio-reactor system[END_REF]. Results proved similar, with total removal of 5-FU, as 5-FU could no longer be quantified at process output. In the present study and even if the LOQ can be considered to be high, the concentrations in the effluent are so high that removal is always higher than 95%. Obviously, each removal rate is calculated from specific measured data. By using batch degradation tests with radiolabeled compounds [START_REF] Mahnik | Fate of 5-fluorouracil, doxorubicin, epirubicin, and daunorubicin in hospital wastewater and their elimination by activated sludge and treatment in a membrane-bio-reactor system[END_REF] noticed total 5-FU removal from the liquid phase and negligible sorption onto the activated sludge, ranging from 2 to 5%.

Thus 5-FU is almost totally removed by biotransformation. The capacity of 5-FU to be biotransformed at low and high concentrations had already been shown by some authors (Kiffmeyer et al., 1998 ;[START_REF] Yu | Occurrence and biodegradability studies of selected pharmaceuticals and personal care products in sewage effluent[END_REF]. However, these results were obtained by conducting tests on high concentrations which do not reflect the actual situation, as there might be an inhibitory effect and the analytical methods used were sometimes unsuitable (measurement of COD or of produced CO2). Thus [START_REF] Kümmerer | Biodegradability of the anti-tumour agent ifosfamide and its occurrence in hospital effluents and communal sewage[END_REF] observed contradictory results: he found no biotransformation of 5-FU for very high 5-FU concentrations (9 and 850 mg.L -1 ). In this present study, degradation kinetics for 5-FU were performed on sludge from the municipal WWTP and on sludge acclimated to OWW in batch reactors for 5 initial concentrations: 50 -1000 µg.L -1 . The kinetics were performed over 21h so as to match the HRT of the eMBR pilot at the time of sampling.

The sludge from the municipal WWTP was adjusted to the concentration of the acclimated sludge, i.e. 4.1 g.L -1 . Whatever the concentration, 5-FU reduction was always slightly greater for acclimated sludge than for sludge sampled from municipal WWTP. In that concentration range the activated sludge from municipal WWTP also seemed very efficient for the removal of 5-FU, as the minimum reduction was always greater than

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 15 80%
. 5-FU reductions by both types of sludge, as well as associated specific degradation velocities are presented in Figure 2.

Just as with sludge acclimated to OWW, the higher the initial concentration, the greater the reduction was, which shows that the concentration range, which was tested (50 -1000 µg.L -1 ) was lower than a possible inhibition threshold; the kinetics remained pseudo-first order. Acclimation to OWW allowed the biomass to be slightly more efficient at initial low concentrations (50 -200 µg.L -1 ). This improvement is minor as 5-FU proved to be a very easily removable molecule. Thus the specific removal velocities of 5-FU were almost the same: 0.0115 gTSS -1 .h -1 for the acclimated sludge and 0.0114 gTSS - 1 .h -1 for the municipal WWTP. However it is very important to note that in spite of the numerous pharmaceuticals, metabolites and cleaning products contained in the OWW which was used for acclimation, the acclimated biomass proved to be at least as efficient as the WWTP sludge, which only removed 5-FU during those tests. Nevertheless the kinetics study allowed for identification of a few behavioral differences in both types of sludge. The degradation kinetic constants kbiol were calculated between t=15 min and t=90 min as the term ln(C/C0) is linear in that range, thus confirming that the degradation kinetics is pseudo-first order for the first 90 minutes (Figure 3).

The evolution of the degradation constant kbiol shows that acclimation at the source allows for faster removal of 5-FU. Thus an average 34% increase was reached for the acclimated sludge, compared with WWTP sludge. The variation of kbiol constants shows that WWTP reached a threshold, while the kbiol constants continue to evolve beyond 1000 µg.L -1 for the acclimated sludge. Hence, it seems that the minimum inhibition concentration is lower for WWTP sludge than for sludge acclimated to OWW in the MBR.

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 16

III.1.2. Performance with pharmaceutical cocktails

The influence of antineoplastics and antibiotics cocktails was quantified in the biomass acclimated to OWW and in a biomass from a municipal WWTP, by monitoring the degradation of an easily biodegradable substrate. Specific degradation velocities of the COD were calculated for each "type of biomass -pharmaceutical cocktail" configuration and were represented according to the COD concentration plotted to the amount of TSS (Figure 4).

On Figure 4, positive and negative velocities respectively show COD degradation and an inhibitory effect exerted by pharmaceuticals. Non-acclimated WWTP sludge (Rousset, France) proved to be totally impacted by the presence of pharmaceutical cocktails: degradation velocity of the COD was zero at low concentrations and even became negative for high charges. A negative degradation velocity means that the presence of pharmaceuticals triggered cell lysis of activated sludge. Conversely, positive COD degradation velocities were measured for sludge acclimated to OWW, which means that it retains a capacity for purification in the presence of pharmaceuticals. However, COD degradation velocities were slower than that of the control group without pharmaceuticals, indicating that pharmaceutical cocktails still partially inhibit the performance of the biomass, which would be logical given the high concentrations used in the cocktail. Optimal degradation velocity was around 0.2 gCOD.gTSS -1 for the acclimated sludge. An inhibition of COD degradation by pharmaceutical materials was observed from 0.1 gCOD.gTSS -1 for the sludge in sole presence of the antineoplastic cocktail and from 0.2 gCOD.gTSS -1 for the antineoplastic cocktail with antibiotics. Thus inhibition seems stronger for the antineoplastic cocktail on its own than for the combination of antineoplastic and antibiotic cocktails. This surprising observation could be due to (i) interactions between antineoplastics and antibiotics, which brought about a

III.2. Removal mechanisms of the selected pharmaceuticals

III.2.1 Sorption

Degradation tests in a batch reactor were performed so as to determine the influence of each of the two removal mechanisms coupled with the purifying biomass: sorption and biotransformation. These tests must show whether the apparent removal with sludge acclimated to OWW is mainly linked to a pollutant transfer from the liquid to the solid phase, or whether there is a biological metabolization by bacteria from the purifying biomass. Lastly, these tests were also carried out using municipal WWTP activated sludge from Rousset, so as to quantify the improvement brought by acclimation of sludge to OWW.

Sorption of the selected pharmaceuticals seemed relatively low for the antineoplastics molecules on the activated sludge of the sMBRe hospital pilot and for the municipal

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT
18 WWTP, as it turned out to be lower than 10% (Figure 5a). Although its sorption remained very low, ifosfamide seems to have more affinities with sorption than cyclophosphamide, which is coherent with Seira's results (2013). Sulfamethazole was removed a few percent more than antineoplastics for both types of sludge, but its sorption remained limited as its removal reached a maximum 13% for sludge acclimated to OWW. Codeine seems to have much more pronounced sorption affinities, since its removal through sorption reached up to 30% for WWTP sludge. Whichever sludge was used, the proportion of sorption of pharmaceuticals tended to decrease as its initial concentration increased, because of a constant number of sorption sites on bacterial flocs for a larger amount of pollutant.

Thus apparent removal of cyclophosphamide, ifosfamide and sulfamethazole by acclimated sludge may be attributed to biotransformation.

Although transfer of pollutant from the liquid phase to sludge appears limited, the very high concentrations in pharmaceuticals in OWW may include significant amounts of pharmaceuticals sorbed onto the sludge of an MBR treating these OWW. These amounts, calculated from average concentrations measured in OWW and from corresponding concentration factors, show that ifosfamide and sulfamethazole might be present in high concentrations in MBR sludge and should be taken into account when choosing the appropriate treatment method for excess sludge (Table 3).

The evolution of distribution coefficients KD according to the concentration factor is logically identical to that of reduction through sorption (Figure 5b). The selected pharmaceuticals have low distribution coefficients KD. KD was smaller than 40 L.kgTSS -1

for CP, IF and SM and smaller than 120 L.kgTSS -1 for CD in municipal WWTP. [START_REF] Joss | Removal of pharmaceuticals and fragrances in biological wastewater treatment[END_REF] claimed that for a value of KD smaller than 300 L.kgTSS -1 sorption of a compound is negligible and its removal may be assessed using input and output concentrations.

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

According to Ternes et al. ( 2004) sorption may be considered as a significant removal mechanism at a threshold value of 500 L.kgTSS -1 . Even though this seems justified for both antineoplastics, SM sorption represents more than 10% of removal for acclimated sludge, and more importantly, sorption of CD allows for a removal above 30%. These observations match Seira's (2013), who noted that low values of KD could not be systematically neglected since sometimes significant removal could occur even for molecules presenting low KD. He proposed to highlight the particle concentrations associated to any suggestion of KD limit value from which sorption may be considered negligible.

Sorption of CP, IF and SM proved to be in the same order of magnitude for both tested activated sludge. The nature of the sludge could have significantly influenced the sorption affinities of a compound, but comparison between the sludge acclimated to OWW and that of the Rousset WWTP provided no evidence of this. This was probably due to the fact that the MBR of the WWTP and that of the MBR which was used for acclimation had the same configuration (submerged external membrane bioreactor).

Another factor could be the origin of the sludge which was used as a base for acclimation, which came from the Rousset WWTP. Comparing several studies would be difficult because of differences in experimental procedures and in the nature of the sludge (Table 4).

KD coefficients of both antineoplastics (CP and IF) found in the literature are generally low (Seira, 2013 ;Ternes et al., 2004). High values of KD for CP of 794. Although sorption of the selected pharmaceuticals may not be totally ignored, it proved to be a minor removal mechanism for acclimated sludge, except for codeine, which could be removed through sorption in proportions ranging around 30%.

III.2.2. Biotransformation

Concerning biotransformation tests, a definite improvement in the total removal of the 4 pharmaceuticals was observed with sludge acclimated to OWW (Figure 6). The initial concentration was analysed. Except for codeine, which was always removed very

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

21 efficiently whatever the concentration factor, total removal of pharmaceuticals with acclimated sludge seemed to increase with their initial concentration. Conversely and except for ifosfamide, removal with sludge from the municipal WWTP seemed to stagnate when initial concentration increased. Capacities for biotransformation were developed by sludge acclimated to OWW while removal of selected pharmaceuticals from the Rousset municipal WWTP sludge was mainly due to a sorption mechanism, as the following ratio shows:

௧௦௧ ௌ௧
smaller than 1 (Table 5).

In accordance with literature, the biotransformation measured for the 2 antineoplastics CP and IF by municipal WWTP sludge proved to be low, even zero [START_REF] Kümmerer | Biodegradability of the anti-tumour agent ifosfamide and its occurrence in hospital effluents and communal sewage[END_REF][START_REF] Buerge | 1st Network Conference on Persistent Organic Pollutants: Human Exposure and Impacts[END_REF]. Removal of CD and SM through biotransformation in the Rousset WWTP sludge was low, as it was in the order of 8% in 4 h. Total removal after 4 h seems to confirm partial removal of these two pharmaceuticals, as mentioned in the literature. The stagnation of removal which was observed for the municipal WWTP sludge could show the inhibition effect exerted by the most concentrated pharmaceutical cocktails. Besides, the 1000 µg.L -1 cocktail of each of these pharmaceuticals seemed to trigger a very strong inhibition, as a significant decrease in the reduction of CD was noted. Thus increase in the removal of CP and IF for the most concentrated cocktail could be due to the sorption of these molecules onto soluble microbial products which were released during a possible cell lysis brought about by the pharmaceutical cocktails in the non-acclimated municipal WWTP sludge. The IANESCO Laboratory is certified and these conclusions are validated by the results of analyses.

These batch reactor tests confirm the correct reductions with 43% maximum removal

M A N U S C R I P T A C C E P T E D

for SM and around 70% for CD in only 4 h (Figure 6). These results agree with those measured in the supernatant of the MBR during the acclimation period of the sludge.

However, maximum removal of CP at 36% and of IF at 38% in that reduced time scale seems better than the removal obtained in the MBR. This could stem from the nature of the substrate, which was far less rich and complex than real OWW, and from the initial absence of these molecules in the supernatant, which was not the case in the MBR. It should also be noted that exclusively aerobic conditions (applied in batch reactors) are known to favor degradation of micropollutants as opposed to aerobic/anoxic processes (applied in the MBR) [START_REF] Suarez | Removal of pharmaceuticals and personal care products (PPCPs) under nitrifying and denitrifying conditions[END_REF], even if a 4 h test remains far shorter than the HRT of the MBR. Although these removals are incomplete, it should be noted that the differences in the associated concentrations are substantial since they are in the order of 700 µg.L -1 for CD and 400 µg.L -1 for SM, CP and IF.

These removals were obtained without adding a cosubstrate in the batch reactors. Thus actual biotransformation of the selected pharmaceuticals could be achieved through a direct metabolic pathway. Seira (2013) did not observe any biotransformation of CP and IF without adding a cosubstrate, but the concentrations tested in his study, respectively 6 and 2 µg.L -1 , were much lower than those of the present study (100 to 1,000 µg.L -1 ).

The higher concentrations used here are representative of the concentrations measured in OWW. Such concentrations could allow pharmaceuticals to be used as primary substrate for the biomass. The data obtained in this study permits to calculate kinetic parameters for removal by both types of tested activated sludge (Eq.2) for each pharmaceutical.

ௗሾ௨௧ሿ ௗ௧ = ݇ • ሾܲℎ݈ܽܽܿ݅ݐݑ݁ܿܽ݉ݎሿ ௧ Eq.2 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 23
kglobal, which is the kinetic constant of total removal measured during the 4h test (µg 1- n .L n-1 .h -1 ), and n, which is the order of the reaction, were then determined through linearization (Eq.3) :

log ଵ ௗሾ௨௧ሿ ௗ௧ = log ଵ ݇ + ݊ • log ଵ ሾܲℎ݈ܽܽܿ݅ݐݑ݁ܿܽ݉ݎሿ ௧ Eq.3
Representing this equation allows for the determination of the kinetics constant kglobal (Table 6).

It appears that some degradation kinetics deviate from pseudo-first order, such as that of cyclophosphamide by acclimated sludge. This deviation may be attributed to experimental uncertainties or to inhibition by the pharmaceutical cocktail at the highest concentrations. However, it seems obvious that the order greater than 2 for IF obtained with the Rousset WWTP sludge is due to the WWTP sludge's inability to biotransformate that compound. It is not possible to use a mathematical model for IF. The traditional model of pseudo-first order was not used and the values of the kinetic constants were not compared to literature data because their units depend on the order of the reaction.

The biotransformation kinetic constant kbiol and the associated order of the reaction n may be determined from concentrations at sorption equilibrium [Pharmaceutical]eq and final [Pharmaceutical]f during the total removal test (Eq.4 and Table 6):

log ଵ ሾ௨௧ሿ ష ሾ௨௧ሿ ௧ ି௧ = log ଵ ݇ + ݊ • log ଵ ሾܲℎ݈ܽܽܿ݅ݐݑ݁ܿܽ݉ݎሿ ௧ Eq.4 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 24
After integrating equation 2, previously determined kinetic constants and orders of reaction allow for the calculation of the residual concentration of pharmaceuticals according to time (Eq.5):

)ݐ(݈݅ܿܽሿݐݑ݁ܿܽ݉ݎܽሾܲℎ = ቂ݇ • ݐ • (݊ -1) + ݈ܽܿ݅ݐݑ݁ܿܽ݉ݎܽሾܲℎ ሿ ௧ (ଵି) ቃ భ భష
Eq.5

Thus it is possible to represent a removal profile for each pharmaceutical for both types of activated sludge. A profile is proposed with the kglobal constant representing maximum removal of a pharmaceutical, when biotransformation does not limit sorption kinetics and allows for the release of sorption sites onto bacterial flocs. Another profile is represented with the kglobal constant for the first 4 hours, then with only kbiol, which would be the most unfavorable case, i.e. sorption which does not repeat because of very low biotransformation, which does not allow for the rapid release of sites onto bacterial flocs.

Final concentration in pharmaceuticals was calculated using Eq.6 up to 4 h then Eq.7 from 4 h respectively, for the profile, which only takes into account the biotransformation mechanism after 4 h.

From 0 to 4 h:

)ݐ(݈݅ܿܽሿݐݑ݁ܿܽ݉ݎܽሾܲℎ = ቂ݇ • ݐ • (݊ -1) + ሾܲℎ݈ܽܽܿ݅ݐݑ݁ܿܽ݉ݎሿ ௧ (ଵି) ቃ భ భష
Eq.6

Then with t > 4 h:

)ݐ(݈݅ܿܽሿݐݑ݁ܿܽ݉ݎܽሾܲℎ = ቂ݇ • ݐ( -4) • (݊ -1) + ሾܲℎ݈ܽܽܿ݅ݐݑ݁ܿܽ݉ݎሿ ସ (ଵି) ቃ భ భష Eq.7 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 25
Removal profiles were calculated using the average concentrations of the selected pharmaceuticals measured in the OWW: 26 -290 -1664 -422 µg.L -1 for codeine (CD) -Cyclophosphamide (CP) -Isofofammide (IF) -Sulfamethoxazole (SM respectively). The temporary absence of a molecule in OWW was not taken into account in the calculation of the average concentration. The evolution of the removal of the 4 pharmaceuticals from the liquid phase was represented for both types of sludge (Figure 7). It should be specified that most of the profiles using kglobal overestimated the removal kinetics of the liquid phase, particularly for the sludge from the Rousset WWTP, as the renewal of sorption sites depends on the biotransformation mechanism. Hence a low biotransformation will limit the sorption kinetics once sorption equilibrium has been attained.

Removal of a pharmaceutical from the liquid phase in a batch reactor is situated between the two curves which were calculated from kglobal and kbiol. So the actual evolution of removal of CD, CP and IF for the Rousset WWTP sludge should follow the curve for removal through biotransformation, as this mechanism limits sorption kinetics. This kinetics is probably close to the kglobal curve for sludge acclimated to OWW, because of the developed biotransformation which is greater than sorption, and close to kbiol for the sludge from the Rousset municipal WWTP. They are the solid lines in Figure 7.

These profiles show the extent of the capacity for biotransformation developed by activated sludge thanks to on-site treatment of OWW. It seems that SM is the pharmaceutical which presents the smallest number of differences between the two types of sludge. The clearest gain from treatment with activated sludge is for the two antineoplastics (CO and IF), as developed biotransformation enabled us to obtain

M A N U S C R I P T A C C E P T E D
ACCEPTED MANUSCRIPT significant removals. Moreover, it is logical to suppose that the profile that best represents removal of antineoplastics for the Rousset WWTP is the profile which only takes into account biotransformation after 4 h, as sorption does not limit the biotransformation mechanism, which proved to be quasi null. Obviously all the removal profiles calculated with the kglobal constant are above the associated profile calculated with the kbiol constant, except ifosfamide for acclimated sludge, whose two profiles are practically superposed. This superposing clearly shows that sorption is renewed as the biotransformation process of IF takes place. Consequently it is possible to determine the time needed to reach a given reduction. The time needed to obtain a 95% reduction was determined for both types of sludge (Table 7).

These results clearly demonstrate the gain from the acclimation in an MBR of sludge acclimated to OWW. However, these durations remain far longer than the average HRT of the sMBRe pilot used for the 29 h acclimation during the experiment. The removals that correspond to this average HRT are presented in Table 8.

This calculated data should be interpreted with caution. Comparing removal kinetics of a batch reactor and of a continuous process may prove delicate, especially as retention of pharmaceuticals by the membrane, as was observed in this study [START_REF] Hamon | Traitement des effluents d'un service d'oncologie par bioréacteur à membranes : faisabilité d'acclimatation et gain apporté sur l'élimination de molécules médicamenteuses[END_REF], strongly limits that comparison. This data, calculated from kinetic parameters, would

show an excellent removal of IF for acclimated sludge. Still, the performance of the hospital MBR pilot which was used for acclimation proved to be consistently lower.

These differences may be attributed to the pharmaceutical cocktail created for the tests in a batch reactor. This cocktail only contained 4 pharmaceuticals, which is far from the great complexity of OWW as to quantity and quality, without even mentioning metabolites. Thus the profile of IF removal by sludge acclimated to OWW may be Removal of CP at average HRT seems more reliable as the 46% removal calculated from kbiol is relatively close to reduction in the MBR during acclimation. It should be noted that subtracting the average sorption part of 3.7% (previously observed for the activated sludge of the hospital sMBRe pilot) from the 46% removal of CP would give a biotransformation part of 42.3%. This biotransformed fraction of 42.3% is in the same order of magnitude as the biotransformation measured by Seira (2013) of 39 ± 5 % in an eMBR pilot treating urban wastewater with a CP dopant. The developed model also seems reliable for SM, as its average removal by the MBR during acclimation (75%) was between the calculated maximum removal and removal through biotransformation.

Lack of data about the sorption of codeine on sludge acclimated to OWW makes it impossible to conclude on the validity of the model for that pharmaceutical.

IV. Conclusion

Removal of the selected pharmaceutical molecules by activated sludge acclimated to OWW and non-acclimated sludge from the municipal WWTP confirms literature observations on the heterogeneity of the removal of pharmaceuticals. 5-FU was almost systematically removed beyond 90%. This molecule is easily removable, as the performance of WWTP sludge confirmed. It is important to specify that (i) even if removal of 5-FU by sludge acclimated to OWW seems identical to that by WWTP sludge, sludge acclimation was obtained in the presence of other inhibitory compounds contained in the effluents of the oncological ward (pharmaceuticals, surface-active

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT
agents), which makes the results all the more remarkable, and (ii) using sludge acclimated to OWW allowed for a 34% increase in the degradation kinetic constant and in the minimum inhibition concentration. Table 5. 4 hours removal of the selected pharmaceuticals by both activated sludge. * Table 6. k and n values for the total and biotransformation removal by both activated sludge.

Table 7. Required duration to achieve 95 % removal of the selected pharmaceuticals for both activated sludge.

Table 8. Removals of the selected pharmaceuticals for both activated sludge at the average HRT of 29h of the sMBRe pilot treating OWW.

First,

  OWW were sent into the denitrification tank (10.5 L) which was stirred through sludge recirculation carried out by a peristaltic pump. The dissolved oxygen concentration is continuously monitored in the denitrification reactor in order to check its zero value. The bacterial flocs were maintained in suspension without aerating the anoxic zone, which allowed the denitrification reaction to take place. A fraction of the recirculated sludge was transferred to the aerobic tank, which has a capacity of 21.5 L, and in which the nitrification reaction took place. The hydraulic retention time (HRT) in both tanks was set through the adjustment of valves, which established 1h/2h cycles in the anoxic and anaerobic tanks respectively. Aeration was performed by fine air bubbles delivered through four porous tubes connected to a compressor. This maintained oxygen concentration above 2 mg.L -1 and ensured the stirring of the aerobic tank. A centrifugal pump (B3, Motovario) located at the foot of the nitrification tank performed suction of the activated sludge towards the membrane module. Two acclimation

8

  The three most consumed antineoplastics in the oncological ward of the Timone hospital are among the seven antineoplastics on the French national agency of sanitary safety of food, environment and work (Anses) list: ifosfamide (IF), fluorouracil (5-FU) and cyclophosphamide (CP). Fluorouracil was analyzed by the pharmacology and toxicokinetics laboratory of the Timone hospital (Marseille, France). Ifosfamide, cyclophosphamide, codeine and sulfamethoxazole were analyzed by the Ianesco laboratory (Institut d'Analyses et d'Essais en Chimie de l'Ouest, Poitiers, France). This laboratory is COFRAC-certified to analyze the specific molecules studied in our paper.

  channel B: methanol, with a flow rate of 0.35 mL.min -1 . The solvent gradient was modified at 0-4/12/16/20/24/25/40 min with the respective ratios (A-B) 95-5/70-30/30-70/2-98/1-99/0-100/95-5/95-5%. The injected volume was 20 μL and the oven temperature was 25°C. The electrospray ionization mode (positive-mode Turbo-V) was positive mode (ion formation [M+H] + mostly but also potentially Na + or K + adducts). The temperature, the acquisition mode, the duration of the MRM windows and the duration of analysis were respectively 550°C, MRM, 200s and 44 minutes. The retention times for codeine, sulfamethoxazole, ifosfamide and cyclophosphamide were respectively 5.4/8.5/10.5/11 min. Prior to analysis, wastewater was decanted then filtered on a 0.45 µm porosity filter. Removal of the coarsest solid materials should not lead to under-estimating the pharmaceutical concentration in OWW, as the selected pharmaceuticals are excreted solely through the urinary tract and are hydrophilic. Thus sorption on TSS of OWW is negligible. The analytical LOQ might seem high regarding pharmaceutical concentration in municipal wastewater but is satisfying regarding the oncological ward wastewater which was investigated.

  sample was taken. Sorption tests were carried out in closed 200 mL brown glass vials slightly stirred to ensure homogeneous mixing and avoid sedimentation of sludge particles. The null value of the dissolved oxygen was checked once every hour during tests. In biotransformation tests degradation kinetics of the selected pharmaceuticals were performed in brown glass vials, aerated over 4h, with sludge acclimated to OWW and WWTP sludge. Similarly to sorption tests, activated sludge concentration in TSS was brought down to 4 g.L -1 . Filtered sludge was resuspended with distilled water. The initial concentrations of the pharmaceutical cocktails were identical to that of the sorption tests, i.e. close to the targeted concentrations 100 -250 -500 and 1000 µg.L -1 , and allowed us to respect the concentration factor 1 -2.5 -5 and 10.

  pharmaceutical toxicity, or (ii) to the absence of toxicity of the antibiotics cocktails on acclimated sludge, hence the differences observed in specific removal velocities of the COD would be due only to experimental uncertainties (COD measurement precision). A contrasting behavior was observed for the municipal WWTP sludge. The antineoplastic cocktail in the presence of antibiotics triggered a more pronounced cell lysis than the sole antineoplastic cocktail, showing that antibiotics have a bactericidal effect on non-acclimated activated sludge. Thus the acclimated biomass acquired resistance to the tested antibiotics and may have been able to metabolize them partially. These results clearly demonstrate that biomass acclimation allowed for the development of capacities of high resistance to antineoplastics and antibiotics, since, at low charge, the sludge developed in the hospital MBR was only slightly affected by their presence.

  3 L.kgTSS -1[START_REF] Delgado | Bioréacteur à membrane externe pour le traitement d'effluents contenant des médicaments anticancéreux : élimination et influence du cyclophosphamide et de ses principaux métabolites sur le procédé[END_REF] and of 111.4 L.kgTSS -1[START_REF] Zaviska | Nanofiltration membrane bioreactor for removing pharmaceutical compounds[END_REF] could be due to the thermic inhibition technique used, which completely breaks down the biomass[START_REF] Hamon | Determination of sorption properties of micropollutants: What is the most suitable activated sludge inhibition technique to preserve the biomass structure?[END_REF]. For activated sludge from an MBR pilot, Seira (2013) obtained similar results to those of this study, with a KD of 15 L.kgTSS -1 for CP and of 22 L.kgTSS -1 for IF. It should Seira's study measured the most pronounced sorption of CP and IF for eMBR sludge. This was attributed to the smaller-sized flocs, which provided more sorption sites to compounds. KD coefficients of SM found in the literature seem very heterogeneous as they range from 3.2 to 370 L.kgTSS -1 . These differences could be explained by the nature of the activated sludge and by the inhibition techniques that were used, but also by SM photosensitivity[START_REF] Hörsing | Determination of sorption of seventy-five pharmaceuticals in sewage sludge[END_REF], which is negligible in this study since sorption tests were carried out in brown glass vials. Average values of 34.1 L.kgTSS -1 measured in this study for sludge acclimated to OWW and of 21.4 L.kgTSS -1 for Rousset WWTP sludge were in the same order of magnitude as KD measured in the studies of Abbeglen (2009) for MBR sludge and[START_REF] Yang | Sorption and biodegradation of sulfonamide antibiotics by activated sludge: Experimental assessment using batch data obtained under aerobic conditions[END_REF] for conventional WWTP sludge. Significant differences were observed for values of KD obtained for CD. Again, these differences could be due to the inhibition techniques that were used and/or to the nature of the activated sludge. It should also be noted that the sorption assessed in this study is competitive as pharmaceuticals were added as a cocktail, while the results of some studies in the literature are sorption values of isolated compounds.

  reasons mentioned above, because of experimental mistakes, or because of the low experimental concentrations used in IF, compared to OWW concentrations, which would only trigger an inhibitory effect restricted to the biomass.

Figure 1 .

 1 Figure 1. Specific removal velocity of 5-FU in eMBR treating OWW.

Figure 2

 2 Figure 2. 5-FU removal in batch reactor for activated sludge from municipal WWTP and

Figure 3 .

 3 Figure 3. Evolution of k biol for activated sludge from municipal WWTP and eMBR treating

Figure 4 .

 4 Figure 4. COD specific removal velocity in presence of pharmaceuticals for activated sludge

Figure 5 .

 5 Figure 5. Removal of the selected pharmaceuticals due to sorption (a) and values of K D for

Figure 6 .

 6 Figure 6. 4hours removal of the selected pharmaceuticals for both activated sludge from

Figure 7 .

 7 Figure 7. Removal evolution of the 4 selected pharmaceuticals by both activated sludge.

Figure 1 .

 1 Figure 1. Specific removal velocity of 5-FU in eMBR treating OWW.

Figure 2

 2 Figure 2. 5-FU removal in batch reactor for activated sludge from municipal WWTP and activated sludge from the eMBR treating OWW.

Figure 3 .

 3 Figure 3. Evolution of k biol for activated sludge from municipal WWTP and eMBR treating OWW.

Figure 4 .

 4 Figure 4. COD specific removal velocity in presence of pharmaceuticals for activated sludge from municipal WWTP and the eMBR treating OWW.

Figure 5 .

 5 Figure 5. Removal of the selected pharmaceuticals due to sorption (a) and values of K D for the selected pharmaceuticals (b) for both activated sludge from municipal WWTP and the sMBRe treating OWW.

Figure 6 .

 6 Figure 6. 4hours removal of the selected pharmaceuticals for both activated sludge from municipal WWTP and the sMBRe treating OWW.

Figure 7 .

 7 Figure 7. Removal evolution of the 4 selected pharmaceuticals by both activated sludge.

Figure 1 .Figure 2 Figure 3 .Figure 5 .

 1235 Figure 1. Specific removal velocity of 5-FU in eMBR treating OWW.

  Acclimated sludge allowed for a 34% increase in the degradation kinetic constant (ii) Acclimated sludge allowed an increase in the minimum inhibition concentration.(iii) Sorption of pharmaceuticals onto sludge proved minor in comparison of biosorption (iv) Removal of pharmaceuticals is attributed to developed biotransformation (v) High removal factor for 5-FU whatever the presence of other inhibitory compounds

Table 1 :

 1 State and performance of activated sludge at the sampling date

Table 2 :

 2 Concentrations of the antineoplastic and antibiotics cocktail

Table 3 :

 3 Quantity of sorbed pharmaceutical onto activated sludge.

Table 4 .

 4 K D values of selected pharmaceuticals reported in previous studies.

	M A N U S C R I P T

Table 5 .

 5 4 hours removal of the selected pharmaceuticals by both activated sludge. * Table6. k and n values for the total and biotransformation removal by both activated sludge.

	Table 7. Required duration to achieve 95 % removal of the selected pharmaceuticals for both
	activated sludge.

Table 8 .

 8 Removals of the selected pharmaceuticals for both activated sludge at the average HRT of 29h of the sMBRe pilot treating OWW.

Table 1 :

 1 State and performance of activated sludge at the sampling date

Table 2 :

 2 Concentrations of the antineoplastic and antibiotics cocktail

Table 3 :

 3 Quantity of sorbed pharmaceutical onto activated sludge.

Table 4 .

 4 K D values of selected pharmaceuticals reported in previous studies.

Table 1 : State and performance of activated sludge at the sampling date

 1

	10

Table 3 : Quantity of sorbed pharmaceutical onto activated sludge.

 3

	Molecule	OWW average (µg.L -1 )	Corresponding CF	Corresponding (%) removal efficiency	Specific quantity of sorbed pharmaceutical (µg.gTSS -1 )
	CD	26	1	31.3 (WWTP)	2
	CP	290	2.5	4.3	3
	IF	1664	10	2.7	11
	SM	422	5	12.3	13

Table 4 . K D values of selected pharmaceuticals reported in previous studies.

 4

	77
	Molecule	KD (L.kgTSS -1 )	Activated sludge origin	Inhibition technique	Reference
		-	sMBRe -infinite SRT	Anaerobic	Present study
		102.1 ± 8.9	sMBRe WWTP	Anaerobic	Present study
	CD	Sorption too low to be quantified	primary -secondary at weak SRT -secondary at long SRT	AS are slightly frozen then sterilized at 103°C during 3h	Hörsing et al., 2011
		14 ± 1	Conventional WWTP	Sodium azide 0.2 % (v/v)	Wick et al., 2009
		9.6 ± 3.8	sMBRe -infinite SRT	Anaerobic	Present study
		17.7 ± 4.0	sMBRe WWTP	Anaerobic	Present study
		15 -12 -0 -	eMBR -Conventional WWTP -Sludge
			thickener -Conventional WWTP -	Gas purging	Seira (2013)
	CP	47 -20 -32	sMBRe -Conventional WWTP
		794.28	eMBR	Thermal	Delgado (2009)
		2.4 ± 0.5	WWTP	Gas purging	Ternes et al.,
		log KD about 3.2 --> KD = 1600	Primary sludge	-	Okuda et al.,
		17.1 ± 2.1	sMBRe -infinite SRT	Anaerobic	Present study
		15.9 ± 0.6	sMBRe WWTP	Anaerobic	Present study
	IF		eMBR -Conventional WWTP -Sludge
		22-71-7-87-55-63	thickener -Conventional WWTP -	Gas purging	Seira (2013)
			sMBRe -Conventional WWTP
		1.4 ± 0.4	WWTP	Gas purging	Ternes et al.,
		34.1 ± 7.3	sMBRe -infinite SRT	Anaerobic	Present study
		21.4 ± 2.0	sMBRe WWTP	Anaerobic	Present study
		256 ± 169	Conventional WWTP	Freeze-drying	Göbel et al., 2005
		3.2 ± 4.5 -77 ± 60 -60 ± 49 -63 ± 42	Primary sludge -Secondary sludge -MBR flat sheets -MBR hollow fibers	Freeze-drying	Radjenovic et al., 2009
	SM	40 ± 13 -50 ± 13	MBR	-	Abbeglen et al., 2009
		320 -370 -280	primary -secondary at weak SRT -secondary at long SRT	AS are slightly frozen then sterilized at 103°C during 3h	Hörsing et al., 2011
		<30	Primary and two different secondary activated sludge	Lyophilization and dry-heat	Stevens-Garmon et al.,
		28.6 ± 1.9	Conventional WWTP	Sodium azide 1 g.L -1	Yang et al., 2011

Table 5 . 4 hours removal of the selected pharmaceuticals by both activated sludge.

 5 Values presented relate the average removals calculated from the 4 concentration factors.

			sMBRe -acclimated to OWW		sMBRe municipal WWTP
	Molecule	Total (%)	Biotrans formation (%)	Sorption (%)	݊݅ݐܽ݉ݎ݂ݏ݊ܽݎݐ݅ܤ ݊݅ݐݎܵ	Total (%)	Biotrans formation (%)	Sorption (%)	݊݅ݐܽ݉ݎ݂ݏ݊ܽݎݐ݅ܤ ݊݅ݐݎܵ
	CD	66.1 ± 5.1	-	-	-	36.6 ± 6.3	7.9	28.7 ± 3.8	0.3
	CP	21.9 ± 10.1	18.2	3.7 ± 1.4	4.9	9.7 ± 2.8	3.1	6.6 ± 1.4	0.5
	IF	26.8 ± 7.9	20.5	6.3 ± 2.5	3.2	5.2 ± 5.6	-0.7	5.9 ± 2.1	Solely sorption
	SM	31.3 ± 9.4	19.4	11.9 ± 1.8	1.6	16.2 ± 2.7	8.4	7.8 ± 1.5	1.1

*

*

Table 6 . k and n values for the total and biotransformation removal by both activated sludge. 83Table 7 . Required duration to achieve 95 % removal of the selected pharmaceuticals for both activated sludge.

 67

			sMBRe/municipal		sMBRe/municipal
	Molecule	sMBRe/OWW	WWTP	sMBRe/OWW	WWTP
		t95% max (h)	t95% bio (h)	t95% max (h)	t95% bio (h)
	Codeine (CD)	16	22	-	94
	Cyclophosphamide (CP)	170	212	408	4849
	Ifosfamide (IF)	44	619	61	Never
	Sulfamethoxazole (SM)	57	86	150	336

Table 8 . Removals of the selected pharmaceuticals for both activated sludge at the average HRT of 29h of the sMBRe pilot treating OWW.

 8

		sMBRe/OWW	sMBRe/municipal WWTP
	Molecule	Max removal	Bio removal	Max removal	Bio removal
		(%)	(%)	(%)	(%)
	Codeine (CD)	99.6	-	98.6	67.0
	Cyclophosphamide (CP)	58.8	46.0	42.9	13.7
	Ifosfamide (IF)	88.6	91.8	71.5	34.0
	Sulfamethoxazole (SM)	84.3	69.3	68.1	46.6

Acknowledgements

The authors acknowledge the Ianesco laboratory (Institut d'Analyses et d'Essais en Chimie de l'Ouest, Poitiers, France) for their help.


Darshan Venkatrayappa
email: darshan.venkatrayappa@mines-ales.fr

Philippe Montesinos
email: philippe.montesinos@mines-ales.fr

Daniel Diep
email: daniel.diep@mines-ales.fr

Baptiste Magnier
email: baptiste.magnier@mines-ales.fr

RSD-DOG : A New Image Descriptor based on Second Order Derivatives

Keywords: Rotating filter, difference of Gaussian, second order image derivatives, anisotropic half Gaussian kernel, image matching

This paper introduces the new and powerful image patch descriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order features/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian (DOG) approach. The obtained descriptor shows a good discriminative power when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC.

Introduction

Local image feature extraction has evolved into one of the hot research topics in the field of computer vision. Extracting features that exhibit high repeatability and distinctiveness against variations in viewpoint, rotation, blur, compression, etc., is the basic requirement for many vision applications such as image matching, image retrieval, object detection, visual tracking and so on. For this purpose, a number of feature detectors [START_REF] Mikolajczyk | Scale & Affine Invariant Interest Point Detectors[END_REF] and descriptors [START_REF] Mikolajczyk | A Performance Evaluation of Local Descriptors[END_REF] have been proposed. In the computer vision literature, features related to first order image statistics such as segments, edges, image gradients and corners have been used in abundance for image matching and object detection. Whereas, features related to second order statistics such as cliff, ridges, summits, valleys and so on have been sparsely used for the image matching and object recognition purpose. The scope of the work lies in the use of second order statistics for the task of image matching.

Related work

In one dimension, the first order gradient extracted at a point gives the slope of the curve at the given point. In case of an image, the first order gradient at a pixel measures the slope of the luminance profile at that pixel. Several local image descriptors such as SIFT [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF], GLOH [START_REF] Mikolajczyk | A Performance Evaluation of Local Descriptors[END_REF], DAISY [START_REF] Tola | DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo[END_REF], and LBP [START_REF] Ojala | Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[END_REF] are based on first order gradient information of the image. And amongst all, SIFT is one of the most widely used local image descriptor. It is constructed by capturing the gradient information at every pixel around an interest point. Extensions of SIFT such as PCA-SIFT [START_REF] Ke | PCA-SIFT: A more distinctive representation for local image descriptors[END_REF], F-SIFT [START_REF] Zhao | Flip-Invariant SIFT for Copy and Object Detection[END_REF], MI-SIFT [START_REF] Ma | MI-SIFT: mirror and inversion invariant generalization for SIFT descriptor[END_REF] are an improved version of the original SHIFT descriptor, by introducing the new invariance properties while using the same first order gradients as their bases. While GLOH [START_REF] Mikolajczyk | A Performance Evaluation of Local Descriptors[END_REF] improves on the robustness and distinctiveness of the SIFT descriptor by using radial binning strategy, Fan et al. [START_REF] Fan | Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor[END_REF] pool the first order image gradients based on their intensity orders in multiple support regions. By doing so, they achieve rotation invariance without actually calculating the reference orientation. DAISY, combines both SIFT and GLOH binning strategy for fast and dense matching. Ojala et al. [START_REF] Ojala | Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[END_REF] came up with local descriptor made of first order binary patterns (LBP) for texture classification. Center-Symmetric LBP (CS-LBP) [START_REF] Heikkila | Description of interest regions with local binary patterns[END_REF] and orthogonal color LBP (OC-LBP) [START_REF] Zhu | Image region description using orthogonal combination of local binary patterns enhanced with color information[END_REF] provides a compact representation of the LBP descriptor while keeping the same discriminative power. Zambanini et al. [START_REF] Zambanini | A Local Image Descriptor Robust to Illumination Changes[END_REF] propose LIDRIC descriptor, based on multi-scale and multi-oriented even Gabor filters. The descriptor is constructed in such a way that typical effects of illumination variations like changes of edge polarity or spatially varying brightness changes at each pixel are taken into account for illumination insensitivity. LIDRIC has a dimension of 768. Oliva et al. [START_REF] Oliva | Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope[END_REF] employ Gabor filters to the grey-scale input image at four different angles and at four spatial scales to obtain the GIST descriptor. The descriptor has a dimension of 512 and is more global. Authors of [START_REF] Venkatrayappa | RSD-HoG: A New Image Descriptor[END_REF] propose a new descriptor called RSD-HoG. It is based on the orientation of the edges. The edge orientations are extracted by using a rotating half Gaussian kernel. The descriptor is constructed in a novel way, by embedding the response of the rotating half Gaussian kernel in a Histogram of oriented Gradient (HoG) framework.

On the other hand, in one dimension second order derivative at a point measures the local curvature at that point i.e. how much the curve bends at that point. The authors of [START_REF] Osadchy | Surface dependent representations for illumination insensitive image comparison[END_REF] use an oriented second derivative filter of Gaussians to capture isotropic as well as anisotropic characteristics of surface by the use of single scale for descriptor generation. Fischer et al. [START_REF] Fischer | Image Descriptors Based on Curvature Histograms[END_REF] proposed a new image descriptor based on second order statistics for image classification and object detection. Their descriptor extracts the direction and magnitude of the curvature and embeds these information in the HOG framework. But, one of the disadvantage of this descriptor is the dimension. Eigenstetter et al. [START_REF] Eigenstetter | Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity[END_REF] have proposed an object representation framework based on curvature self-similarity. This method goes beyond the popular approximation of objects using straight lines. However, like most of the descriptors using second order statistics, this approach also exhibits a very high dimensionality.

As shown in Fig. 1, An image represented by a two dimensional function can be considered as a surface in a 3D space. Such a surface in 3D, consists of features such as ridges, valleys, summits or basins. The geometric properties of these features can be accurately characterized by local curvatures of differential geometry through second order statistics. The motivation behind this work is to extract these 2nd order image statistics and represent them as a compact image descriptor for image matching. The main contribution of this work is:

1. The idea was to consider the 2D image patch as 3D surface made of ridges, valleys, summits, etc and to extract these second order statistics by using a local directional maximization or minimization of the response of difference of two rotating half smoothing filters. 2. These directions correspond to the orientation of ridges, valleys or a junction of ridges/valleys. The orientations at which these second order statistics occur, are binned to form a local image descriptor RSD-DOG of dimension/length 256. By construction, the dimension of the descriptor is almost 3 to 4 times less when compared to other descriptors based on second order statistics. 3. This descriptor is evaluated for invariance to blur, rotation, compression, scale and viewpoint changes. Additionally, by construction, the descriptor shows enormous robustness to variations in illumination. To highlight this property, we rigorously evaluate the descriptor on dataset consisting of images with linear and non-linear illumination changes.

The remainder of the paper is organized as follows. In the section 2, we present a directional filter made of anisotropic smoothing half Gaussian kernels. In section 3, we present our robust method for extracting the ridge/valley directions using difference of half directional Gaussian filters. Section 4 is devoted to descriptor construction process and section 5, discusses about experiments and results. Finally, section 6 concludes this paper with the future work.

Y X 1 2 _ 1 2 _ θ Y X 1 2
+--

Directional Filter

In our method, we use a directional filter made of anisotropic smoothing half Gaussian kernels. For every pixel in the image patch, we spin this directional filter to obtain a Rotating Signal Descriptor (RSD), which is a function of a rotation angle θ and the underlying signal. As shown in [START_REF] Montesinos | A New Perceptual Edge Detector in Color Images[END_REF] and [START_REF] Magnier | Texture Removal by Pixel Classification using a Rotating Filter[END_REF], smoothing with rotating filters means that the image patch is smoothed with a bank of rotated anisotropic Gaussian kernels:

G (µ,λ) (x, y, θ) = C.H R θ x y e -x y R -1 θ   1 2 µ 2 0 0 1 2λ 2   R θ   x y   ( 1
)
where C is a normalization coefficient, R θ a rotation matrix of angle θ, x and y are pixel coordinates and µ and λ are the standard-deviations of the Gaussian filter. As we require only the causal part of the filter (illustrated on figure 2(a)), we simply "cut" the smoothing kernel at the middle, and this operation corresponds to the Heaviside function H [START_REF] Montesinos | A New Perceptual Edge Detector in Color Images[END_REF]. By convolving the image patch with these rotated kernels (see figure 2(b)), we obtain a stack of directional smoothed image patches

I θ = I * G (µ,λ) (θ).
To reduce the computational complexity, in the first step we rotate the image at some discrete orientations from 0 to 360 degrees (of ∆θ = 1, 2, 5, or 10 degrees, depending on the angular precision required and the smoothing parameters) before applying non rotated smoothing filters. As the image is rotated instead of the filters, the filtering implementation can use efficient recursive approximation of the Gaussian filter. As presented in [START_REF] Montesinos | A New Perceptual Edge Detector in Color Images[END_REF], the implementation of the method is clear and direct. In the second step, we apply an inverse rotation of the smoothed image and obtain a bank of 360/∆θ images. 3 Ridge and valley detection using difference of Gaussian filters

Difference of Half smoothing filters (DHSF)

At every pixel in the image patch, we are required to estimate a smoothed second order derivative of the image along a curve crossing these pixels. In one dimension, the second order derivative of a signal can be easily estimated using a Difference Of Gaussian (DOG)operator [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF]. In our method, as in [START_REF] Magnier | Ridges and Valleys Detection in Images Using Difference of Rotating Half Smoothing Filters[END_REF], we directly apply two half Gaussian filters with two different λ and the same µ to obtain the directional derivatives. An example for the two discretized filters is shown in Fig. 2(d). Later, we compute the difference of the response of these two filters to obtain the desired smoothed second order derivative information in the ridge/valley directions (illustrated in Fig. 3(b)). We refer to this half Gaussian filter combination as the difference of half smoothing filters (DHSF). An illustration of DHSF is presented in Fig. 3(a) and Fig. 2(c).

Estimating the direction of second order statistics such as ridges and valleys

By convolving the image patch with the DHSF (by the technique of rotating the images, as explained above), for each pixel in the image patch we obtain a pixel signal which captures a directional second order information around the pixel. The idea is to extract the directions at which these second order statistics such as ridges and valleys occur and to construct a descriptor from this information. Let us consider D(x, y, θ) to be the pixel signal obtained at pixel P located at (x, y). D(x, y, θ) is a function of the direction θ such that:

D(x, y, θ) = G (µ,λ1) (x, y, θ) -G (µ,λ2) (x, y, θ) (2)
µ, λ 1 and λ 2 correspond to the standard-deviations of the Gaussians. At each pixel in the image patch, we are interested in the response of the DHSF at θ M1 , θ M2 , θ m1 and θ m2 . Where, θ M1 and θ M2 are the directions at which the On a typical valley (point 1 in Fig. 4), the pixel signal at the minimum of a valley consists of at least two negative sharp peaks. For ridges (point 7 in Fig. 4), the pixel signal at the maximum of a ridge contains at least two positive peaks. These sharp peaks correspond to the two directions of the curve (an entering and leaving path). In case of a junction, the number of peaks corresponds to the number of crest lines (ridges/valleys) in the junction (point 4 in Fig. 4). We obtain the same information for the bent lines (illustrated in point 2 on Fig. 4). Finally, due to the strong smoothing (parameter µ), D is close to 0 in the presence of noise without any crest line nor edge (illustrated in point 10 in Fig. 4). This illustrates the robustness of this method in the presence of noise.

DESCRIPTOR CONSTRUCTION

The descriptor construction process as shown in Fig. 5, in the initial stage, as in [START_REF] Mikolajczyk | Scale & Affine Invariant Interest Point Detectors[END_REF], for each detected key-point we follow the standard procedure to obtain the rotation and affine normalized gray level image patch. This normalization procedure is followed in the construction of all the descriptors (SIFT, DAISY, GLOH) used in our experiments. We consider this image patch as a 3D surface, with intensity being the 3rd dimension. As in Fig. 5, for each pixel in the image patch, we spin the DHSF and obtain a stack of DOG patches. From this stack of DOG patches, for each pixel we extract the signal D(x, y, θ) (for simplicity and proper viewing, in Fig. 5 signal is not shown and a stack of image patch is shown). From each signal we extract the four angles θ M1 , θ M2 , θ m1 , θ m2 and their corresponding responses ||D(x, y, θ M1 )||, ||D(x, y, θ M2 )||, ||D(x, y, θ m1 )|| and ||D(x, y, θ m2 )||. Once these informations are obtained, for each pixel P, we estimate the average angles η 1 and η 2 and there respective average magnitudes δ 1 and δ 2 by:

       η 1 (x, y) = (θ M1 + θ M2 )/2 η 2 (x, y) = (θ m1 + θ m2 )/2 δ 1 = (||D(x, y, θ M1 )|| + ||D(x, y, θ M2 )||)/2 δ 2 = (||D(x, y, θ m1 )|| + ||D(x, y, θ m2 )||)/2
The angle η 1 is weighed by δ 1 and η 2 by δ 2 and binned as in Eq. 3. Later, H η1 and H η2 are concatenated to form the final 256 length/dimension RSD-DOG descriptor.

H η1 = {η 1 bin1 , η 1 bin2 , η 1 bin3 , η 1 bin4 .........η 1 bin128 } H η2 = {η 2 bin1 , η 2 bin2 , η 2 bin3 , η 2 bin4 .........η 2 bin128 } (3)
5 Experiments and Results

Dataset and Evaluation

Matlab platform is used for the experiments. Harris affine key points [START_REF] Mikolajczyk | Scale & Affine Invariant Interest Point Detectors[END_REF] were used for image patch extraction as well as the key points obtained from other detectors can also be used for extracting these image patches. We evaluate and compare the performance of our descriptor as against the state of the art descriptors on the standard dataset, using the standard protocol provided by the Oxford group. The binaries and dataset are publicly available on-line 1 . The dataset used in our experiments has different geometric and photometric transformations, such as, change of scale and image rotation (boat), viewpoint change (graf), image blur (bike), JPEG compression (compression) and illumination change (Leuven). For each type of the image transformation, there is a set of six images with established ground truth homographies. In-order to study in detail the performance of our descriptor for changes in illumination, we also evaluated our descriptors on four image pairs, with complex illumination changes and the data set for the same is publicly available 2 . The complex illumination dataset has 4 set of images, namely 'desktop', 'corridor', 'square' and 'square root'. The first two sets, 'desktop' and 'corridor' have drastic illumination changes, whereas 'square' and 'square root' datasets are obtained by a square and square root operation on the second image of the 'desktop' set [START_REF] Wang | Local Intensity Order Pattern for Feature Description[END_REF]. Some of the image pairs from both datasets are shown in Fig. 7.

The evaluation criterion used as proposed by [START_REF] Mikolajczyk | A Performance Evaluation of Local Descriptors[END_REF], is based on the number of correspondences, correct matches and false matches between two images. Here, we test the descriptors using similarity threshold based matching, since this technique is better suited for representing the distribution of the descriptor in its feature space [START_REF] Mikolajczyk | A Performance Evaluation of Local Descriptors[END_REF]. Due to the space limitation, we restrain from going into the details of this method. A detailed description of this method can be found in [START_REF] Mikolajczyk | A Performance Evaluation of Local Descriptors[END_REF]. The results are presented using the recall vs 1-precision curves. As in Eq.4, recall is defined as the total number of correctly matched affine regions over the number of corresponding affine regions between two images of the same scene. From Eq.5, 1-precision is represented by the number of false matches relative to the total number of matches. In all our experiments, Euclidean distance is used as the distance measure.

Our descriptor depends on 5 different parameters: ∆θ, N o-of -bins, µ, λ 1 and λ 2 . The rotation step ∆θ is fixed to 10 • . Increasing the rotation step results in loss of information. As in [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF] the image patch is divided into 16 blocks. All blocks are of the size 10x10 (Since we are using a patch of size 41x41, the blocks in the extreme right and bottom have 11x11 size). As in [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF], the number of bins (N o-of -bins) is fixed to 8 per block, resulting in a 8 * 16 = 128 bins for 16 blocks. Increasing the number of bins results in same performance as in previous case, but it increases the dimensionality of the descriptor. Filter height µ is fixed to 6. As in [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF], for DHSF the ratio between successive scales is fixed to √ 2. So, filter widths λ 1 and λ 2 are fixed to 2 and 2 √ 2 respectively. In our experiments, we obtain state of art results by using just two scales. Height (µ = 6) and Width (λ 1 = 2, λ 2 = 2 √ 2) parameters are chosen to have a ratio sharpness length that is suitable for robust second order feature detection [START_REF] Montesinos | A New Perceptual Edge Detector in Color Images[END_REF], which generally gives good results in most cases. This ratio is compatible with the angle filtering step.

Descriptor Performance

In our experiments, we have used two variations of our descriptor ( 1 The performance of these two variants of RSD-DOG is compared with the performance of SIFT, GLOH, DAISY, GIST and LIDRIC descriptors. For SIFT and GLOH, the descriptors are extracted from the binaries provided by Oxford group. For DAISY descriptor, the patches are extracted from the code provided by 3 . The matlab code for GIST and LIDRIC descriptors were obtained from4 and5 respectively.

For changes in rotation, viewpoint, blur and compression both variants of the RSD-DOG shows better performance than the other 5 descriptors. The precision vs (1-recall) plots in the first 4 rows of the Fig. 8 illustrates the superiority of our descriptor. Image pair graf(1-5) is a complex image pair. As a result, performance of all the descriptors deteriorates. It should be noted that, in most of the cases, RSD-DOG (3-SCALES) performs similar to or slightly better than that of RSD-DOG (2-SCALES). So, increasing the number of scales increases the complexity and descriptor dimension with very little gain in performance. For variations in illumination, in all cases, both the variants of RSD-DOG performs consistently better than all the other descriptors. When it comes to 'square' and 'square root' images SIFT, DAISY, LIDRIC and GIST descriptors exhibit poor performance and GLOH descriptor fails miserably. The graphs in the last 4 rows of Fig. 8, illustrate the superior nature of our descriptor for complex illumination changes.

Conclusion

The paper proposes a novel image patch descriptor based on second order statistics such as ridges, valleys, basins and so on. The originality of our method lies in combining the response of directional filter with that of the Difference of Gaussian (DOG) approach. One of the advantage of the proposed descriptor is the dimension/length. Our descriptor has a dimension of 256, which is almost 2 to 4 times less than other descriptors based on second order statistics. The experiments on complex illumination dataset illustrates the robustness of our descriptor to complex illumination changes. On the standard dataset provided by the Oxford group our descriptor outperforms SIFT, GLOH, DAISY, GIST and LIDRIC. In the future, we would like to use our descriptors for applications related to object detection, classification and image retrial. Additionally, we would like to learn the parameters by introducing a learning stage. The speed of the descriptor generation can be boosted by parallel programming.

Fig. 1 .

 1 Fig. 1. (a) Image representation in 2D. (b) and (c) The same image being represented in 3D, with intensity being the third dimension. Both (b) and (c) when viewed from different angles, exhibits a set of second order statistics such as ridges, valleys, summits, edges etc.

  Smoothing filter (b) Rotating filters (c) DHSF. (d) Discretized DHSF.

Fig. 2 .

 2 Fig. 2. A smoothing rotating filter and Difference of Half smoothing Filters (DHSF). For (d) top: µ = 10 and λ = 1. For (d) bottom: µ = 10 and λ = 1.5.

Fig. 3 .

 3 Fig. 3. DHSF filter descriptions.

10 Fig. 4 .

 104 Fig. 4. Points selection on a synthetic image. Examples of functions D(x, y, θ) on the points selected on synthetic image using µ = 10, λ1 = 1, λ2 = 1.5. The x-axis corresponds to the value of θ (in degrees) and the y-axis to D(x, y, θ).

Fig. 5 .

 5 Fig. 5. Methodology involved in the construction of RSD-DOG descriptor.

Fig. 6 .

 6 Fig. 6. (a) η1 computation from θM 1 and θM 2 . (b) η1 corresponds to the direction perpendicular to the ridge/valley at the level of a pixel P.

  matches No of correct matches + No of false matches

1Fig. 7 .

 7 Fig. 7. A few examples of image pairs used in our evaluations. Images in the first column (a) has variations in blur (BIKE), second column (b) has changes in rotation (BOAT), third column (c) has compression changes, fourth column (d) has changes in view-point (GRAFF). Images in the fifth, sixth and seventh column have variations in illumination( (e) LUEVEN, (f) CORRIDOR, (g) DESKTOP, (h) SQUARE-ROOT ).

  ) RSD-DOG (2-SCALES) with height µ = 6 and width λ = 2, 2 √ 2 respectively. This has a dimension of 256. (2) RSD-DOG (3-SCALES) with height µ = 6 and width λ = 2, 2 √ 2, 4 respectively. Here, in step one, we smooth the image patch with µ = 6, λ 1 = 2 and λ 2 = 2 √ 2 to obtain a 256 length descriptor. In the second step, we smooth the image patch with µ = 6, λ 1 = 2 √ 2 and λ 2 = 4 and obtain another 256 length descriptor. Lastly, we concatenate the two parts to form a 512 size RSD-DOG(3-SCALES) descriptor.

Fig. 8 .

 8 Fig. 8. Recall vs 1-Precision curves for SIFT, GLOH, DAISY, GIST, LIRDC and RSD-DOG. Similarity matching is used for evaluation.

http://cvlab.epfl.ch/software/daisy

http://people.csail.mit.edu/torralba/code/spatialenvelope/

http://www.caa.tuwien.ac.at/cvl/project/ilac/

Acknowledgements

The work is funded by L'institut Mediterraneen des Metiers de la Longevite (I2ML).