Removal of micropollutants in domestic wastewater by expanded granular sludge bed membrane bioreactor

Journal Pre-proof Removal of micropollutants in domestic wastewater by expanded granular sludge bed membrane bioreactor ´ Clara V. Faria, Barbara C. Ricci, Ana F.R. Silva, Miriam C.S. Amaral, Fabiana V. Fonseca

PII:

S0957-5820(19)32305-5

DOI:

https://doi.org/10.1016/j.psep.2020.01.033

Reference:

PSEP 2091

To appear in:

Process Safety and Environmental Protection

Received Date:

14 November 2019

Revised Date:

19 January 2020

Accepted Date:

24 January 2020

Please cite this article as: Faria CV, Ricci BC, Silva AFR, Amaral MCS, Fonseca FV, Removal of micropollutants in domestic wastewater by expanded granular sludge bed membrane bioreactor, Process Safety and Environmental Protection (2020), doi: https://doi.org/10.1016/j.psep.2020.01.033

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Removal of micropollutants in domestic wastewater by expanded granular sludge bed membrane bioreactor Clara V. Fariaa,*, Bárbara C. Riccib,c, Ana F.R. Silva c, Miriam C. S. Amaralc, Fabiana V. Fonsecaa a

School of Chemistry, UFRJ, Federal University of Rio de Janeiro, Av. Athos da Silva Ramos 149, Cidade Universitária, RJ, 21941-909, Brazil. (E-mail: [email protected]; [email protected]) b

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Department of Chemical Engineering, Pontifical Catholic University of Minas Gerais, Av. Dom José Gaspar, 500, Coração Eucarístico, Belo Horizonte, MG, 30535-901, Brazil. (E-mail: [email protected]) c

School of Engineering, UFMG, Federal University of Minas Gerais, Av. Antônio Carlos, 6627, Pampulha, Belo Horizonte, MG, 31270-901, Brazil. (E-mail: [email protected]; [email protected])

1 Corresponding author at: Avenida Athos da Silva Ramos 149, Cidade Universitária, RJ, 21941909, Brazil. E-mail address: [email protected]

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Highlights

The EGSB reactor removed three of seven pharmaceuticals compounds

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The EGSB-MBR system showed higher removals of COD and pharmaceuticals

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Biodegradation, sorption and membrane retention contributed to the high

UF membrane decreased the final effluent toxicity and risk to human health

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removals

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ABSTRACT

Sewage treatment plants effluent is considered the primary source of many micropollutants in aquatic systems since their biological treatment is commonly unable to remove persistent micropollutants. However, its efficacy can be achieved with the aid of advanced treatment technologies, such as membrane processes. This work evaluated the removal efficiency of 7 pharmaceuticals (Ketoprofen, Prednisone, Fenofibrate,

Fluconazole, Betamethasone, Loratadine and 17α-Ethinyl estradiol) in a hybrid system (EGSB-MBR) where an ultrafiltration membrane was submerged in an anaerobic expanded granular sludge bed (EGSB) reactor. This integrated system improved the removal efficiencies of pharmaceuticals (> 84%) and chemical oxygen demand (COD). The EGSB reactor alone showed COD reductions around 92%, while the EGSB-MBR system achieved COD reductions above 98%. Furthermore, the permeate showed lower concentrations of nutrients (P, N-NH4+) and volatile fatty acids (VFAs) than the effluent from the anaerobic reactor alone. Anaerobic biodegradability tests, together with bioreactor results, pointed out the mechanisms involved in the removal of each drug. The

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risk assessment showed that the permeate presented a low probability of risk to human

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health and that the UF membrane was able to reduce the risk of the final effluent.

Keywords: EGSB; Membrane bioreactor; Pharmaceuticals; Anaerobic digestion; Ultrafiltration; Risk assessment 1. Introduction Over the last few decades, the occurrence of micropollutants in the aquatic environment has become an issue of increasing environmental concern. They are called “micropollutants” because commonly they are present in waters at concentrations, ranging from ng L-1 to μg L-1. In this class of compounds there are pharmaceuticals,

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personal care products, hormones, industrial chemicals and pesticides. Hormones and pharmaceuticals are widely used for the treatment of adversities that affects humans and animals and, so, they end up entering the aquatic environment.

Research has been developed to study and understand what the real effects are on

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short- and long-term exposure to these micropollutants. Recent studies have already proven that these compounds may cause adverse effects, including aquatic toxicity,

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microorganisms' resistance to antibiotics and endocrine disruption (Pruden et al., 2006). In addition, these hormones and pharmaceuticals have already been detected in animal,

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plants and human tissues (Bexfield et al., 2019).

The different properties of pharmaceutical compounds, such as hydrophobicity, and biodegradability make the removal of these compounds difficult in conventional

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wastewater treatment plants (WWTPs). The current WWTPs are not able to provide a complete barrier for micropollutant removal (Tambosi et al., 2010; Chtourou et al., 2018). For this reason, WWTPs’ effluent has been considered the primary source of many

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micropollutants in aquatic systems (Luo et al., 2014). The elimination of such pollutants is affected by many factors, for instance, raw sewage’s dilution ratio and temperature,

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hydraulic and solid retention times and, mainly, the sewage treatment plant configuration. While enhanced degradation of organic contaminants, removal of nitrogen species

and eliminating the need for tertiary treatments have been implicated in membrane bioreactors (MBRs) as a result of the unique operating conditions including the long solid retention time and enrichment of specific functional genes/enzymes (Calderón et al., 2012; Mukherjee et al., 2018), further consideration should be given to the efficacy of MBRs in treatment of micropollutants (e.g., pharmaceuticals, nanoparticles and 3

antibiotics) (Ma et al., 2018). This is important if direct or indirect potable reuse is proposed for the treated water. Anaerobic membrane bioreactors (AnMBR) have been gaining popularity due to their intrinsic advantages over aerobic systems, such as low sludge production, methane generation, and treatment of high-strength organic wastewaters containing refractory and toxic compounds (Mortezaei et al., 2018), besides their advantages over conventional anaerobic processes, which include longer solid retention time (SRT), the rejection of high molecular weight organics, which are further degraded, enrichment of specific functional genes/enzymes (Ma et al., 2018) and better effluent quality (Abargues et al.,

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2012). According to Monsalvo et al. (2014), the main micropollutants removal mechanism in AnMBR was is biodegradation, but sorption to biomass and fouling layers play also an important role. The analysis of the micropollutants retained has revealed that

micropollutants rejection by the fouling layers over the membranes contribute to the

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improvement of micropollutants removal by AnMBR. Other works also highlight the micropollutants removal efficiency by AnMBR under different conditions, as low temperatures and with powdered activated carbon (PAC) (Huang et al., 2018; Hu et al.,

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2017; Xiao et al., 2017).

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The membrane in an AnMBR also contributes to the complete retention of microorganisms in the bioreactor, that associated with the longer SRT, typically adopted in MBR, contributed to the microbial adaptation to micropollutant, improving their

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removal by selecting microorganisms capable of co-metabolize these compounds. Harb et al. (2016) observed an increased expression of genes associated with aromatic compound metabolism (e.g. CoA-dependent ligases) during prolonged exposure to

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micropollutants in AnMBR.

Although several comparative studies on the removal of micropollutants in AnMBRs

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are available and have shown promising results, there is a need to further improve the AnMBR performance in term of micropollutants removal and membrane fouling mitigation.

The expanded granular sludge bed reactors (EGSB) which combines effluent recirculation and taller bioreactors design (or a high height/diameter ratio), were developed to overcome problems that may occur in the UASB (Upflow Anaerobic Sludge Blanket), such as preferential flows, hydraulic short cuts and dead zones (Seghezzo et al., 4

1998). The EGSB allows the granular sludge bed’s expansion, improving the biomass– wastewater contact (Sheldon and Erdogan, 2016) and requires less space for deployment (Petropoulos et al., 2016). Due to the high recirculation rate applied to the EGSB, this reactor has proven to be suitable for the treatment of toxic, inhibitory and recalcitrant compounds (Dastyar et al., 2015), since the inlet is diluted to levels no longer stressful for bacterial activity with the recirculation (Zoutberg and Been, 1997). An EGSB reactor used in wastewater treatment with antibiotics showed amoxicillin removals of up to 80% and the amoxicillin's concentration in the wastewater had no influence on the reduction of COD (Meng et al., 2015).

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The combination of the membrane and EGSB reactor in the EGSB-MBR may benefit biomass retention and achieve a high-quality effluent (Chu et al., 2005). The membrane location at the top of the reactor reduces the fouling potential since the membrane is not in direct contact with the sludge. In addition, the good settleability of the granular biomass

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reduces the fouling potential and the recirculation contributes to favorable hydrodynamic

conditions to mitigate concentration polarization. Chu et al. (2005) evaluated the efficiency of using an EGSB coupled to a UF membrane to treat sewage. These authors

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observed that at temperatures above 15 °C, the system was able to remove 85-96% of COD. At 11 °C and increasing hydraulic retention time (HRT) from 3.5 to 5.7 h, total

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COD removal increased from 76 to 81%. In addition, they also noted that the application of a higher upflow velocity contributed to better effluent removal efficiency and increased

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membrane permeability.

In the present work, the removal efficiency of 7 pharmaceuticals (Ketoprofen, Prednisone, Fenofibrate, Fluconazole, Betamethasone, Loratadine and 17α-Ethinyl

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estradiol) in a hybrid system (EGSB-MBR) where an ultrafiltration membrane was submerged in an EGSB reactor was investigated and compared to performance of a

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conventional EGSB. Furthermore, the effect of the organic matter availability on the removal of micropollutants in domestic wastewater by the systems was also investigated. The efficacy of the systems was evaluated in terms of micropollutants, COD and nutrients removal efficiency, volatile fatty acids production and membrane fouling. In addition, the effluent and the permeate risk assessments were performed to predict their adverse effects for specific populations. This is the first study in which the micropollutants removal was evaluation in an AnMBR comprised of an EGSB and that long-term filtration tests were performed. 5

2. Materials and methods 2.1. Operational system EGSB reactor was made of acrylic with a working volume of 2.72 L, 1.5 m high, an inner diameter of 0.06 m, and four sampling points (Fig. 1a). For the EGSB-MBR (Fig. 1b), the membrane module was made of poly (vinyl chloride) with 0.04 m inner diameter, 0.12 m high, and 0.045 m2 of filtration area. The hollow-fiber ultrafiltration (UF) membranes were purchased from GE MembranesTM. They were made of PVDF (polyvinylidene fluoride), with a nominal pore size of 0.04 micron and surface with non-

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ionic and hydrophilic properties.

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Fig. 1. Schematic representation (a) EGSB reactor and (b) EGSB-MBR

The filtration unit (Pam Membranas) was operated automatically and, for every 15

minutes of filtration, 15 seconds of backwashing was performed. The operation was carried out under mesophilic conditions (25±4°C) and the upflow velocity was 3.8 m h-1. 2.2. Inoculation Inoculation was carried out with sludge from an UASB reactor used in the city of Belo Horizonte's sanitary sewage treatment (Onça wastewater treatment plant/Copasa – 6

Belo Horizonte/MG). A total of 592.3 g of sludge were added in the EGSB reactor, resulting in 36.76 g g-1 TSS (Total Suspended Solids), 17.33 g g-1 FSS (Fixed Suspended Solids) and 19.01 g g-1 VSS (Volatile Suspended Solids). For the inoculation of EGSB-MBR was used EGSB's granular sludge, solids analysis resulted in 12.16 g L-1 TSS, 8.90 g L-1 VSS and 3.26 g L-1 FSS. 2.3. Stages of operation After inoculation, the reactor was fed with synthetic sewage for about 50 days to promote the microorganisms’ reactivation and adaptation. This stage was called

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acclimatization stage (S1) and, in the following stages of operation (S2 and S3), which lasted 130 days each, the pharmaceutical compounds were introduced continuously to the reactor feed.

In the first stage (S1) the reactor was fed with synthetic sewage and the specific

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organic loading rate (SOLR) applied was 19.46 mg COD gVSS-1 d-1.

The addition of pharmaceutical products to the synthetic sewage took place from the

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stage S2. This stage was characterized by an increase in the SOLR applied to the reactor (113.16 mg COD gVSS-1 d-1). The ideal SOLR for the starting of an anaerobic reactor

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using synthetic sewage as a substrate should be 120 mg COD gVSS-1 d-1 (Campos and Anderson, 1992). The additional SOLR required was achieved by raising the influent’s COD.

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The third stage (S3) was characterized by a decrease in influent’s COD to evaluate the effect of organic matter availability in the micropollutants removal. The SOLR

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applied to the reactor were 85.38 mg COD gVSS-1 d-1. 2.4. Synthetic sewage

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The synthetic sewage had a total COD concentration range of 347.51 ± 124.19 mg

L-1 in the stage S1, 848.30 ± 327.89 mg L-1 in the stage S2 and 618.79 ± 156.93 mg L-1 in the stage S3. The composition used to simulate real domestic sewage is represented in Table 1 (Gomes et al., 2015).

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Table 1 Composition of synthetic sewage. Concentration (mg L-1)

Component

S2

S3

Sucrose

47.8

95.6

71.7

Starch

148

296

222

Cellulose

47.2

94.4

70.8

Beef extract

215

430

322.5

NaHCO3

200

400

300

KH2PO4

120

240

180

NaCl

250

500

CaCl2

7

14

MgCl2

4.5

9

Oil

51

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S1

375

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2.5. Pharmaceutical compounds

Seven different pharmaceuticals compounds were analyzed: Betamethasone,

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17α-Ethinyl estradiol, Fenofibrate, Fluconazole, Ketoprofen, Loratadine, and Prednisone (Table 2). All the compounds were purchased from Sigma Aldrich. The selection of these drugs was based on their previous detection in surface and groundwater, drinking water

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or domestic wastewater (Jelic et al., 2011).

A stock solution with concentrations of 10 mg L-1 of each drug in methanol was

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prepared and maintained at -20°C to avoid degradation. In the reactor feed, this solution was added to the synthetic sewage in an amount big enough to generate final concentrations of 2 µg L-1, which is close to the ones found in real sanitary sewage

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(Heberer, 2002).

The sorption potential of the pharmaceutical compounds was measured by the pH-

dependent octanol-water distribution coefficient (Dow). All Dow data were obtained from Chemicalize (chemicalize.com) at a pH of 7.4 (Table 2). Compounds with log Dow values greater than 2-3 have a high tendency to be adsorbed, while low sorption is predicted for compounds with log Dow values below 2-3 (Ma et al., 2018).

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Table 2 Properties of the selected pharmaceutical compounds. Pharmaceutical

Molecular

CAS

Compound

formula

Number

Log Dow

Molecular

Therapeutic

weight

Group

(g mol-1) C22H29FO5

378-44-9

1.68

392.467

Anti-inflammatory

Ketoprofen

C16H14O3

22071-15-4

0.39

254.285

Anti-inflammatory

C20H24O2

57-63-6

3.90

296.41

Hormone

Fenofibrate

C20H21ClO4

49562-28-9

4.28

Loratadine

C22H23ClN2O2

79794-75-5

4.55

Fluconazole

C13H12F2N6O

86386-73-4

Prednisone

C21H26O5

53-03-2

2.6. Batch experiments

Lipid Regulator

382.888

Antihistamine

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360.834

306.277

Antifungal

1.66

358.434

Anti-inflammatory

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17α-Ethinyl estradiol

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Betamethasone

In order to evaluate pharmaceutical removal mechanisms, anaerobic biodegradability

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tests were conducted. These tests were realized according to the methodology described by the Organization for Economic Cooperation and Development in its protocol OECD

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311(OECD, 2006) and Musson et al. (2010). Two trials were performed in triplicate. For these, 6 amber bottles of 500.0 mL were used, and in each bottle were added a concentration of 20.0 μg L-1 of pharmaceuticals, a

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solution with 10 g of total organic carbon per liter of PEG 400 (Polyethylene glycol 400) to reach an organic carbon concentration of 50.0 mg L-1, and 400 mL of nutrient solution. It was used a headspace corresponding to 20% of total volume. In addition, the sludge added in each flask had a total solids concentration of about 2.0 g L-1 (OECD, 2006). PEG 400 was used as an additional source of carbon to ensure its availability in the degradation process of the pharmaceuticals evaluated. The nutrient solution was prepared with ultrapure water previously de-oxygenated with nitrogen gas for approximately 20 9

minutes (OECD, 2006). The nutrient solution composition was presented in Tables S1 and S2 of Supplementary Material. In the first trial, the sludge used was biologically active and in the second was autoclaved, according to Table 3. The purpose of the autoclaving process was to evaluate the pharmaceuticals removal by abiotic mechanisms, such as chemical degradation and/or adsorption, for this, the inoculum was previously autoclaved for 30 minutes, at 120 ºC. Each bottle was closed with a rubber septum and an aluminum seal. In sequence, the anaerobic environment was imposed from the flow of inert gas (N2) for 20 minutes, by opening the flap of the metal seal and introducing a gas inlet needle and an outlet needle

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in rubber cap.

Table 3. Operation conditions of each test

Condition

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Trial 1

Inoculum, easily digestible substrate (PEG) and pharmaceuticals

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Autoclaved inoculum, easily digestible substrate (PEG) and

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pharmaceuticals

After that, the cap hole was silicone sealed and flasks were incubated at 35 ºC during

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56 days.

2.7. Physicochemical analysis

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The influent, effluent and permeate samples were analyzed periodically for the reactor monitoring. COD (Method 5520), pH (Method 4500-H), and solids (Method 2440) were evaluated according to APHA (2012). The VFAs were monitored according to Dilallo

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and Albertson (1961) and alkalinity according to Dilallo and Albertson (1961) modified by Ripley et al. (1986). The average diameter of the granules was monitored on a laser dispersion particle size

distribution analyzer (Model LA-950, Horiba). 2.8. Pharmaceuticals’ identification and quantification

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For the determination and quantification of pharmaceutical products, the samples were concentrated by solid-phase extraction. For this purpose, 800 mL of sample was filtered in a normal vacuum filtration apparatus with white band quantitative filter paper (Quanty, JP40). The material retained in the filter was discarded and the permeated volume was transferred to an amber flask for the concentration step. For this procedure, cartridges (Strata C18-E, 55 μm, 70 A, Ref. 8B-S001-HCH) were placed in a Manifold (Applied Separations) and conditioned with 5 mL of methanol (Baker Analyzed® HPLC Solvent) followed by 5 mL of ultrapure water (Gehaka) (Gros et al., 2013). After conditioning the extraction cartridge without allowing it to dry, the sample percolated it in order to concentrate the analytes of interest. The percolation velocity was around 4 mL

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min-1. Finally, the cartridge was rinsed with 10 mL of ultrapure water to remove salts and left in the manifold under vacuum for 20 min to remove excess water. Then, the cartridges were stored in a freezer at -20 °C.

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Before the samples were analyzed on high performance liquid chromatography–mass spectrometry (HPLC/MS) the analytes were eluted with 4 mL of methanol and stored in 40 mL glass vials. For the injection on HPLC/MS, 1.0 mL aliquots were filtered on

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Syringe-driven filters (Jetbiofil) and transferred to 1.5 ml amber glass vials.

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The Shimadzu HPLC model Prominence DGU/20A3 was used for the pharmaceutical compounds’ quantification. The chromatographic column used was a 2 mm diameter, 50 mm length and 2 μm particle size Shimadzu C18 model Shim-pack XR-ODS. The

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temperature used in the column was 35 °C, the sample injection volume was 5μL and the flow rate of the mobile phase was 0.2 mL min-1. The mobile phase was composed of a mixture of solvents, containing: solvent A (water with 0.1% formic acid) and solvent B

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(acetonitrile with 0.1% formic acid). To begin the method, 5% of B was introduced which, after 5 minutes, increased to 50%. Subsequently, this percentage of B was kept constant

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for 6 minutes. Before being raised to 70% it kept constant for 8 minutes. Pharmaceuticals were identified in their respective retention times, which are: Ketoprofen (8.0 min), Prednisone (6.5 min), Fenofibrate (17.3 min), Fluconazole (5.3 min), Betamethasone (6.8 min), Loratadine (7.1 min) and 17α-Ethinyl estradiol (8.2 min). After 4 analyzes, the column was cleaned using the following procedure: 0-7 min 100% B; 7-9 min from 100 to 50%; 9-14 min 50%; 14-16 min from 50 to 5%; 16-22 min 5%.

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The micrOTOF-QII mass spectrometer (Bruker) with an electrospray ionization source (ESI) was used. The analyzes on such mass spectrometer were performed in positive mode, with 0.4 bar nebulizer pressure, 180 °C drying gas temperature and 4500V spray voltage. The compounds’ identification was based on their patterns’ retention time when inject into the work matrix. Confirmation was performed in an independent second injection. The response ratios between different transitions were calculated and compared to the standard ones. This method is in accordance to the one described by European Commission Decision 2002/657/EC. To assess the compliance of ion ratios, the following equation was used: 𝑆𝑎𝑚𝑝𝑙𝑒 𝐼𝑜𝑛 𝑟𝑎𝑡𝑖𝑜 − 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐼𝑜𝑛 𝑟𝑎𝑡𝑖𝑜 ∙ 100% 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐼𝑜𝑛 𝑟𝑎𝑡𝑖𝑜

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𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 =

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The 2002/657/EC limit ion ratio tolerance is presented in Table 4.

Ion ratio tolerances of 2002/657/EC.

Relative tolerance

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Ion ratio

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Table 4

± 20%

0.20 – 0.50

± 25%

0.10 – 0.20

± 30%

< 0.10

± 50%

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> 0.5

2.9. Membrane chemical cleaning process The membrane chemical cleaning process occurred with the use of a sodium

hypochlorite solution (1000 ppm) and a citric acid solution at pH 2.5. First, the module was submerged in the hypochlorite solution for 20 min in an ultrasound bath. The module was then left for 20 min in backwashing using the same hypochlorite solution. And,

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finally, the module was submerged in the citric acid solution and left for another 20 min in ultrasound. The resistance-in-series model was applied to evaluate the characteristics of membrane fouling. Darcy's law was used to describe the permeate flux and determine the resistance:

𝐽(𝑡) =

∆𝑃 𝜇(𝑅𝑚 + 𝑅𝑓 )

where J is the permeate flux (m3 (m2 h)-1), P the transmembrane pressure (Pa), μ the

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permeate viscosity (Pa s), Rm is the intrinsic membrane resistance (m−1), and Rf the fouling resistance (m−1). This last parameter can be associated with cake layer formation, pore narrowing and blocking, and/or adsorption (Lee et al., 2003). 2.10. Environmental and human health risk assessment

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The effluent and permeate’s environmental risks were evaluated according to the

hazard quotient (HQ). HQ values were calculated for acute and chronic effects by

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dividing the measured environmental concentration (MEC) by the predicted non-effect concentration (PNEC). For the assessment of the acute environmental risk, the PNEC was

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calculated from dividing the mean effect or lethal concentration data (EC50 or LC50) by 1000. And for the evaluation of chronic environmental risk, the PNEC was determined

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from the division of the non-observed effect concentration (NOEC) by 10 (WHO, 2011). The environmental risk was determined according to the HQ value and classified as high risk (HQ > 1), medium risk (0.1 < HQ < 1), low risk (0.01 ≤ HQ < 0.1) and negligible

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risk (HQ < 0.01) (DWI, 2007). Whenever possible, the E(L)C50 and NOEC values were collected in literature for three trophic levels (algae, crustacean and fish) (Tables S3 and

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S4).

Concerning human health risk, the margin of exposure (MOE) for the effluent and the

permeate was calculated by dividing the drinking water equivalent level (DWEL) by the MEC. The DWEL was calculated from the following equation:

𝐷𝑊𝐸𝐿 =

𝑇𝐷𝐼 ∗ 𝑏𝑚 ∗ 𝑓 𝐶

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Where TDI (tolerable daily intake) values for each pharmaceutical compound were found in literature or determined from no-observed adverse effect level (NOAEL) divided by 100 (European Commission, 1996) (Table S5); bm is the body mass, considered 60 Kg; f is the relative contribution of water to exposure, which can be considered 100% since pharmaceutical products exposure from other sources is insignificant; and C is the daily water consumption, which can be considered 2 L (DWI, 2007). MOE values above 100 indicate a low risk probability (USEPA, 2000). 2.11. Statistical tests Hypotheses about the mechanisms involved in the removal of each pharmaceutical

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were raised using Cluster analysis. To verify if the removals of some parameters in one stage of operation were superior to the removals of the other stage, we used Mann-

Whitney and Kruskal-Wallis non-parametric tests. The statistical analyses were performed using Statistica 10.0 software and all the analyses was performed by the level

3.1. System’s performance

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Results and discussion

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of significance (α) of 5%.

At acclimatization stage (S1), the reactor was operated for approximately 50 days

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with HRT of 45.33 hours and with a SOLR of 19.46 mg COD gVSS-1d-1. At the S2 stage, influent COD, VFAs, alkalinities and SOLR increased. In addition, COD removal increased from 75.91 ± 24.80% to 90.79 ± 5.66 %. This stage lasted almost 130 days with

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HRT of 13.51 hours (Fig. 2 and Fig. 3).

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The last step of operation (S3) presented a decrease in influent´s COD. Consequently, it also presented lower SOLR, alkalinities and VFAs (Fig. 2 and Fig. 3). However, the COD removals and the HRT remained in the same magnitude as in step S2 (Fig. 2). MannWhitney's statistical test showed no significant differences in COD removal when changing step S2 to S3. When analyzing reactor performance according to COD removal, it can be observed that the association of UF membrane contributed to an increase in such percentage, at stage S2 the UF membrane increased COD removal by 8% and at stage S3 by 6% (Fig. 14

2). Moreover, greater stability of the system was observed, since smaller variations in COD removal values were found. This can be evidenced from variation coefficients values, which were 1.86 (S2) and 1.74 (S3) in the permeate, while in the effluent these

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values were 5.66 and 5.18 for the stages S2 and S3 respectively.

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Fig. 2. Effluent COD and COD removal of the EGSB and EGSB-MBR at the three stages of operation

The organic matter rejection by UF membrane is controlled by size exclusion,

electrostatic repulsion, and interactions aromaticity/hydrophobicity between the organic matter and the surface of the membrane (Cho et al., 1999). A study by Jung et al. (2006) showed that hydrophobic organics are adsorbed more rapidly than the hydrophilic organic ones in the UF membrane.

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According to Mann-Whitney analysis the permeate also presented lower values of alkalinities (Fig. 3). Considering stage S3, for example, the effluent total average alkalinity was 304.07 ± 91.55 mg CaCO3 L-1 while in permeate was 187.18 ± 60.17 mg

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CaCO3 L-1.

Fig. 3. VFA and total alkalinity monitoring of the EGSB and EGSB-MBR at the three stages of operation

Regarding the VFAs in the stage S3, an average concentration of 17.06 ± 21.96 mg HAc L-1 was observed in the effluent while 10.27 ± 11.63 mg HAc L-1 was observed in the permeate (Fig. 3). However, Mann-Whitney statistical analysis showed no significant difference between these two samples. 16

Prior to pharmaceuticals addition the granules had average diameters around 965.85 µm, at stage S2 the granules had average diameters equal to 1341.67 ± 341.78 µm and at stage S3 average diameters equal to 1128.08 ± 186.98 µm. According to the statistical analysis of Kruskal Wallis, it can be concluded that the addition of drugs did not compromise the average size of the reactor granules since the average diameter of the granules not varied significantly. Exposure to toxic conditions, sudden organic load, changes in temperature and pH, has been reported to cause flake breakage and result in particle size decrease in AnMBRs (Shen et al., 2015). Meng et al. (2015) observed a decrease in flake size after the addition

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of carbamazepine and fluoroquinolone antibiotics in MBRs and this led to an increase in membrane fouling. However, this was not observed in the present work, since the size of the granules was not compromised.

Concerning the concentration of phosphorus and ammoniacal nitrogen, it was verified

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that the UF membrane, even though it is a porous membrane, contributed to these compounds’ reduction (Table 5).

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In anaerobic environments, it is observed the establishment of a community of microorganisms called phosphorus accumulating organisms (Wang et al., 2008), due to

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these microorganisms, the phosphorus concentration in the effluent was very similar to the influent. The lower values of phosphorus concentration in the permeate can be explained by the formation of the cake on the membrane’ surface, which probably

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Table 5

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contributed to the retention of this compound (Nir et al., 2009).

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N-NH4+ and P concentration in Influent, Permeate and EGSB reactor effluent. P (mg L-1)

N-NH4+(mg L-1)

Influent

75.72±6.68

3.73±0.19

EGSB-MBR permeate

58.18±6.88

8.00±0.11

EGSB effluent

77.04±7.08

13.60±1.30

N-NH4+ is usually found at high levels in the effluents of anaerobic digesters since it is a product of the anaerobic decomposition of organic nitrogen. This explains the fact 17

that higher ammoniacal nitrogen concentrations are observed in the effluent and the permeate when compared to the concentration of this compound in EGSB reactor´s influent. Although N-NH4+ is soluble in water and therefore permeable by the UF membrane (Ledda et al., 2013), retention of approximately 40% of this compound was observed, which can also be attributed to the formation of the cake on the membrane’s surface (Table 5). 3.2. Membrane performance The average permeate flux was 4.15 L h-1 m-2. Fig. 4 summarizes the UF membrane temporal data of permeability and resistance. When the permeability reached less than

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10% of its initial value, the membrane module was removed from the bioreactor and

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cleaned.

Fig. 4. Membrane permeability and resistance monitoring throughout the operation

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This happened only after 70 days of membrane operation. The increase in membrane resistance after the chemical cleaning indicated that the membrane permeability was not fully recovered due to formation of irreversible fouling. This result highlights the importance of using the maintenance cleaning strategy to prevent the irreversible fouling occurrence. Hermia model was used to interpret the fouling phenomenon occurring in ultrafiltration. The fitting of experimental data to the models make it possible to predict if the permeate flux decline is controlled by cake layer formation or pore blocking (standard, intermediate and complete blocking).

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Table 6 shows the fitting of the experimental results of Hermia model by means of k

and R2. R2 is the correlation coefficient for the model, the higher values of R2 correspond

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to a better model fit.

Table 6

k (m) 0,064 0,011

R2 0,896 0,983

Cake k (m) 0,123 0,020

R2 0,924 0,828

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S2 S3

Complete blocking

Models for flux decline Intermediate Standard blocking blocking 2 k (m) R k (m) R2 0,028 0,953 0,055 0,974 0,009 0,947 0,005 0,977

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Stage

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Fouling parameters evaluation by applying Hermia model.

At the S2 stage, the higher R2 value indicates the best fit for the intermediate blocking,

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which means that some particles may have obstructed some membrane pores without blocking it completely (Blanket et al., 2006) (Table 6).

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However, at S3 stage, the complete blocking´s R2 value is very similar to the standard

blocking´s R2 value (Table 6). The standard blocking mechanism occurs when solute molecules approach the membrane and are adsorbed and deposited on the internal pore walls of it, thereby reducing the pore opening (Vela et al., 2008). The complete blocking model considers that the fouling occurs when the size of the solute molecules in the feed solution is greater than membrane pores. Therefore, pore blocking takes places over the membrane surface and not inside of its pores (Vela et al., 2008).

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According to Fig. 4, from the beginning of stage S3 (approximately day 178), it was possible to observe increased membrane resistance and decreased permeability. The evaluation of the fouling by the Hermia model corroborates with these observations since in the S3 stage it was observed that the model fitted well to two blocking types (complete and standard), so these two mechanisms may be guaranteeing analyte retention at this stage.

3.3. Pharmaceutical compounds removal The pharmaceutical compounds were added with a concentration of 2 μg L-1 in the

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influent from day 52. The performance of the biological process was not significantly affected by adding the pharmaceuticals in the influent given the negligible COD removal

variations in the through the experiment (Fig. 2). The Mann-Whitney's statistical test

indicated no significant differences between the COD removals before and after the

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addition of pharmaceuticals in the influent (p > 0.05).

Fig. 5 illustrates the pharmaceuticals removal, by the EGSB and EGSB-MBR, in the

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stages S2 and S3. The trends of Betamethasone, Fenofibrate, and Prednisone removal were similar. These pharmaceuticals were completely degraded and/or retained in the

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EGSB and EGSB-MBR system, in both stages, while the other pharmaceuticals removal was influenced by the type of reactor and/or organic matter availability. The EGSB-MBR showed higher pharmaceuticals removal efficiency justified by the membrane

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contribution.

The removal of pharmaceuticals in biological reactors could be associated with their degradation or sludge adsorption. When the membrane is integrated into the biological

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process, an additional contribution to pharmaceuticals removal could be attributed to their retention by the membrane and/or dynamic membrane formed in the membrane surface

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due to the fouling by cake formation. The complete sludge retention in the membrane bioreactor also contributes to its better performance to remove pharmaceuticals. To elucidate the mechanism involved in the removal of each pharmaceutical, in the EGSB and EGSB-MBR, batch experiments in biotic and abiotic conditions were performed (Fig. 6). Results showed that the betamethasone, ketoprofen and prednisone, despite being of the anti-inflammatory class, present divergences regarding the removal pathways. 20

Furthermore, betamethasone and prednisone have very close log Dow, 1.68 and 1.66, respectively (Table 2), which could suggest similar removal pathways for both. However, it was observed that betamethasone was predominantly removed by biotic mechanisms (95%), whereas prednisone by abiotic mechanisms (98%) (Fig. 6). The high removal of betamethasone and prednisone in the batch assay are in accordance with the removal of

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these pharmaceuticals in the EGSB and EGSB-MBR systems (Fig. 5).

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Fig. 5. Variation in the pharmaceuticals’ removal efficiency in the EGSB and in the EGSB-MBR at stages S2 and S3 of operation

For ketoprofen, however, no significant removal was observed by any of the

mechanisms involved in the study (Fig. 6). Its log Dow of 0.39 already suggests a tendency for low sludge adsorption (Ma et al., 2018). As well as ketoprofen, fluconazole, which is of the antifungal class, was not expressly removed by any mechanism studied (Fig. 6). Fluconazole also has a low log Dow value (0.56), which already reveals a low tendency 21

for adsorption in the sludge. In addition, the fluconazole molecule has an electron withdrawing group (EWG) on the aromatic ring, which may confer certain refractoriness

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to anaerobic degradation (Ghattas et al., 2017).

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Fig. 6. Pharmaceuticals removal mechanism in batch experiments

During the monitoring of EGSB and EGSB-MBR reactors in the stage 2, low removal of ketoprofen and fluconazole was observed in the EGSB reactor (1% fluconazole

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removal and no ketoprofen removal), while in the EGSB-MBR a slight removal of these compounds (12 and 1,4 % for ketoprofen and fluconazole respectively) was observed.

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The removal of this compound in the EGSB-MBR can be attributed to UF membrane or dynamic membrane retention.

Some studies exploring AnMBR also reported low

removals for ketoprofen (15 - 27%) (Hu et al., 2017; Wijekoon et al., 2015). However,

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when amount of available organic load was reduced, stage S3, the removal of ketoprofen and fluconazole in EGSB was increased to 72 and 70%, respectively, while in the EGSBMBR the ketoprofen removal increased to 94 and the fluconazole removal increased to 84%. This observation suggests that the biological degradation of these compounds was favored by the low viability of easily biodegradable substrate. The contribution of the membrane remains observed in stage 3.

22

An EGSB reactor was used to evaluate the removal of a surfactant (Linear Alkylbenzene Sulfonate - LAS) from commercial laundry wastewater. The authors noted that when this wastewater was diluted with larger volumes of sewage, the lower was the removal of surfactant, i.e., as the availability of organic matter increased the removal of surfactant decreased (Faria et al., 2017). The results observed by these authors agree with the results observed in this work, since with the decrease of the organic load, the drug removal was increased. An analysis of the EGSB-MBR reactor supernatant was performed to confirm the membrane retention of pharmaceuticals. The results showed that the membrane had a

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small contribution to the retention of ketoprofen and fluconazole. Removal of 89% of ketoprofen was observed in the supernatant liquid, while removal of 92% after the UF

was observed. For fluconazole, 87% removal was observed in the supernatant liquid while 89% removal was observed by the membrane.

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Loratadine and fenofibrate were mostly removed by abiotic mechanisms (Fig. 6). This may be explained by the high log Dow values, 4.55 and 5.28 respectively, that confer

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hydrophobic characteristics to these compounds. These results explain the high removal of fenofibrate in the EGSB and EGSB-MBR reactor and the no influence of the viability

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of easily biodegradable substrate in the loratadine removal in both systems (Fig. 5). The Mann Whitney's non-parametric test indicated no significant difference in loratadine removal in stages S2 and S3, which reinforce that its removal is not related to

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biodegradation but to sorption. When analyzing the data from the EGSB-MBR system, an effective contribution of the UF membrane to the loratadine removal was observed. This contribution may be by retention of the pharmaceuticals or even by complete

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retention of sludge.

The 17α-ethinyl estradiol hormone removal was attributed to both biotic (73%) and

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abiotic (8%) mechanisms (Fig. 6). Some studies have shown that sorption of 17α-ethinyl estradiol in biological sludge is a thermodynamically favorable process (Xu et al., 2008). The 17α-ethinyl estradiol removal in the EGSB and EGSB-MBR systems was similar, thus, there was no contribution of membrane rejection. In stage S2 the removal efficiency remained around 47% when moving to stage S3 the removal efficiency increased to 88%, which may have been caused by an increase in removal via biotic mechanism due to the reduction of available organic load. The membrane contribution was verified by EGSB-

23

MBR supernatant analysis. An 86% removal of this hormone was observed both in the supernatant fluid and after the UF membrane. Alvarino et al. (2014) compared the performances of two biological reactors, UASB and activated sludge, and observed that the average removal of 50% of 17α-ethinyl estradiol was mostly attributed to biotic mechanisms. Similarly, Monsalvo et al. (2014) using an AnMBR found that about one-third of the total percentage of 17α-

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ethinylestradiol removal, approximately 20%, was attributed to biotic mechanisms.

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Fig. 7. Dendrogram of EGSB reactor effluent in step S3 regarding variables: SOLR and pharmaceutical removal

Fig. 7 shows the EGSB effluent's dendrogram in the operating step S3. For the

construction of this dendrogram, the following variables were related: pharmaceutical removal and SOLR. In the interpretation of a dendrogram the greater the proximity between the measurements, the greater the similarity between them. The dendrogram

24

ranks similarity so that it becomes possible to have a view of the similarity or dissimilarity of a set of samples. By analyzing Fig. 7, it was possible observed that the pharmaceuticals most similar to SOLR were the drugs that are predominantly removed by adsorption (prednisone and fenofibrate). Thus, pharmaceutical's removal and SOLR are inversely related, i.e., decreasing the SOLR applied to the anaerobic reactor increased the removal of drugs, and especially those that are adsorbed. The authors suggest that there was a competition between pharmaceuticals and organic matter for adsorption sites, and by decreasing the

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load of organics the sites available for drug adsorption increased.

Fig. 8. Dendrogram of EGSB-MBR permeate in step S3 regarding variables: SOLR, resistance and pharmaceutical removal

In addition, this Cluster analysis demonstrates relationships between the pharmaceuticals compounds. Ketoprofen and Fluconazole removals have similarity, which may show similarities in the conditions and mechanisms of removal of these

25

compounds. The same is observed between Fenofibrate and Prednisone, however with higher linkage distance. Similar behavior is observed with EGSB-MBR permeate (Fig. 8). Prednisone, fenofibrate and loratadine were more similar to SOLR, as observed also in the effluent (Fig. 7). These drugs are mostly removed by abiotic mechanisms, as evidenced by batch testing. They were related to SOLR, since the reduction of SOLR in step S3 increased the adsorption sites available to effective the adsorption of these pharmaceutical compounds. Fluconazole and ketoprofen were not removed in batch tests by either biotic or abiotic mechanisms. According to the dendrogram of Fig. 8, these drugs have greater similarity

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with the membrane resistance, so their removal may be associated with the retention by the UF membrane.

Regarding the hormone, 17α-ethinyl estradiol, in batch testing it was removed by both abiotic and biotic mechanisms. In the EGSB-MBR system, there is greater similarity

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of the hormone with membrane resistance, which shows that its removal/retention is more

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related to the UF membrane than to SOLR or even biodegradation.

Betamethasone showed low similarity with both SOLR and membrane resistance,

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since this drug is mainly removed by biotic mechanisms.

Thus, this study evidences the efficiency of the EGSB-MBR system for removing some emerging organic microcontaminants. Furthermore, it highlights the importance of

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the correct availability of micropollutant/easily digestible organic substrate in order to achieve greater removal efficiencies of micropollutants, besides the membrane

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contribution.

3.4. Environmental and human health risk assessment

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The synthetic sewage´s risk assessment showed that the concentrations of pharmaceuticals characterized this effluent as high risk to human health and high acute and chronic toxicity, mainly due to the drugs: Fenofibrate, Fluconazole, Ketoprofen, Loratadine and 17α-ethinyl estradiol (Fig. 9 and Fig. 10). An analysis was carried out considering the best-case scenario, which is the one with the largest pharmaceutical’s removal. In this scenario, the EGSB effluent showed low chronic toxicity and medium acute toxicity because of the Loratadine´s HQ value, while the EGSB-MBR permeate showed low acute and chronic toxicities (Fig. 9). 26

This result underscores the importance of the membrane in the treatment of this effluent, since the membrane was able to increase Loratadine rejection and decrease the

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effluent toxicity.

Fig. 9. Influent, Effluent and Permeate acute and chronic toxicities considering the highest pharmaceuticals removal case

27

Concerning the human health risk assessment, the permeate showed a small human health probability of risk (Fig. 10), and only the 17α-ethinyl estradiol concentration in the EGSB effluent presented a risk probability to human health. For the pharmaceuticals that were not confirmed in the effluent or permeate, the

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method detection limits were used for the risk calculations (Fig. 9 and Fig. 10)

Fig. 10. Influent, Effluent and Permeate MOE values considering the highest pharmaceuticals removal case

4.

Conclusions

28

The EGSB-MBR hybrid system showed higher removals of COD (> 98%) and pharmaceuticals compounds (> 84%) when compared to the conventional system (EGSB). The permeate of this system presented lower concentrations of VFA’s and nutrients (P and N-NH4+). In EGSB-MBR system 3 mechanisms of removal of pharmaceutical compounds were highlighted: biodegradation, sorption and membrane retention. Betamethasone, Fenofibrate, Prednisone and Loratadine have been completely degraded and/or retained in the EGSB-MBR system. Fenofibrate, Prednisone were mostly removed by sorption mechanisms, while betamethasone was biodegraded. Loratadine was also mostly

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removed by sorption, however, contribution by membrane retention was observed. Ketoprofen and fluconazole were biodegraded when the organic load available to the microorganisms was reduced, moreover, the membrane was able to retain them, thus elevating their removal.

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membrane did not contribute to its retention.

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17α-ethinyl estradiol removal occurred by both biodegradation and sorption, and UF

In addition, through the risk analysis it was concluded that the UF membrane was

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able to minimize permeate toxicity and lower its risk to human health. The results of this work also underscore the importance of the correct availability of the micropollutant/easily digestible organic substrate in order to obtain higher

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micropollutant removal efficiencies. Therefore, thinking of a two-stage system, where the focus of the first would be the removal of organic matter and the latter the pharmaceuticals' removal, becomes a promising alternative for the effective removal of

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these organic micropollutants.

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Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments The authors gratefully acknowledge the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for granting financial resources. 29

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