Can the removal of pharmaceuticals in biofilters be influenced by short pulses of carbon?

Journal Pre-proof Can the removal of pharmaceuticals in biofilters be influenced by short pulses of carbon?

Nadia Brogård Nord, Kai Bester PII:

S0048-9697(19)35896-6

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135901

Reference:

STOTEN 135901

To appear in:

Science of the Total Environment

Received date:

3 September 2019

Revised date:

4 November 2019

Accepted date:

1 December 2019

Please cite this article as: N.B. Nord and K. Bester, Can the removal of pharmaceuticals in biofilters be influenced by short pulses of carbon?, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.135901

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© 2019 Published by Elsevier.

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Can the removal of pharmaceuticals in biofilters be influenced by short pulses of carbon?

Nadia Brogård Nord, Kai Bester*

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Department of Environmental Science, Aarhus University, Frederiksborgvej 399, Roskilde

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4000, Denmark

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*Corresponding author: e-mail: [email protected]

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ABSTRACT Biofilters, similar to those already used for, e.g., removing particles from stormwater and combined sewer overflow can remove organic micropollutants from polluted waters. This study investigated the effects on removal of pharmaceuticals with pulse loadings of increased amounts of pre-settled raw wastewater to four individual biofilters containing different materials (sand, filtralite, stonewool, and sand amended with 1% peat). The effect of

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increasing BOD concentration to the removal rate constants could be divided into two groups;

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1) compounds influenced by increasing loading of BOD: atenolol, propranolol, venlafaxine,

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citalopram, metoprolol, iohexol, and diclofenac 2) compounds only little or not influenced by

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increasing concentration of BOD: sulfamethoxazole, sulfamethizole, trimethoprim, iomeprol, and carbamazepine. Though BOD clearly had effects on the degradation, no indications

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towards a complete stop of the degradation were observed under any circumstances. The

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different biofilter materials influenced (indirectly) the removal of micropollutants: While the overall best performance was seen in the filtralite biofilter, the stonewool biofilter generally

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had the lowest removal rate constants. Furthermore, we observed different metabolic pathways of metoprolol in the four different biofilters under formation (and removal) of metoprolol acid, α-hydroxymetoprolol, and O-desmethylmetoprolol.

Keywords: Micropollutants, wastewater, biofilm reactor, combined sewer overflow, β-blockers, transformation products, stormwater

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1. Introduction Biofilters are biologically active porous systems utilizing natural biofilms to clean water. In principle several similar systems are mentioned in the literature: retention soil filters (vertical infiltration systems for stormwater treatment), constructed wetlands (CW) (vertical or horizontal systems for wastewater treatment), underground passage (slow (10-30 d ) vertical flow systems for purifying, e.g., river water for successive abstraction and use for drinking

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water), and bank filtration systems (more rapid (0.5-5 d) vertical infiltration systems for river

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water for successive abstraction and use for drinking water) (Andresen and Bester, 2006;

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Bertelkamp et al., 2016; Brix and Arias, 2005; Le Coustumer et al., 2012). In such systems,

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contaminated water is filtered through a porous matrix, e.g., sand and gravel, on which biofilms are grown, which assist in improving water quality. These systems can in principle

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also have macrophyta (plants) growing on the top surface which can have positive effects on

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removing nutrients and metals. They require little process control and maintenance and are thus suited for decentralised wastewater treatment, i.e., as polishing step for small wastewater

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treatment plants, to treat combined sewer overflow (CSO), and stormwater. Even though biofilters are usually built to manage particle loads, organic carbon and nutrients such as phosphorus and nitrogen (Hatt et al., 2009) they are often quite efficient in removing micropollutants (Bester et al., 2011; Carpenter and Helbling, 2017; Casas and Bester, 2015; Hellauer et al., 2018; Reungoat et al., 2011). Important design parameters of biofilters are: hydraulic loading (residence time), loading with easily degradable carbon as expressed in biological oxygen demand (BOD) and water saturation (Benner et al., 2013). Another important property considering filter support materials is how much biofilms can be grown per volume as the (inner) surface to volume ratio is a crucial parameter affecting removal efficiency (Paredes et al., 2016; Ejhed et al., 2018).

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1.1 Decentralised wastewater treatment/very small treatment plants Even though small WWTPs (<100 person equivalent (PE)) only treat minor amounts of water these waters are usually as polluted with organic micropollutants as water from big WWTPs as these micropollutants stem from household activities (Casas and Bester, 2015; Matamoros et al., 2009; Rossi et al., 2013). However, the quality of infrastructure and availability of qualified personnel at small wastewater treatment plants is often lower than in big ones.

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Therefore, new technologies need to be more self-relying and rugged for smaller treatment

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plants. – On the other hand, space is usually of less importance for the smaller treatment

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plants than for the big urbanised one as they are usually located in rural areas. Thus, biofilters may be a better solution for removing micropollutants in a polishing step for these smaller

carbon.

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1.2 Storm water runoff

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plants than more high-tech solutions such as advanced oxidation or sorption to activated

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Changes in rainfall patterns caused by increased global warming are giving heavier and more frequent rain events. Storm water can be contaminated by various substance such as biocides leached from building materials (Bollmann et al., 2014), and PAHs deposited by traffic on roads that become mobilised by rainfall ( Vezzaro et al., 2015). In pilot studies it has been demonstrated that biofilter systems might be suited to remove multiple compound groups from stormwater (Bester et al., 2011; Stachel et al., 2010). The suggested biofilters could be placed at the outlets of, e.g., stormwater ponds. 1.3 Combined sewer overflow (CSO) In combined sewers both untreated wastewater and stormwater is collected in the same pipes. In case of strong rain events, the capacity of these systems can become exceeded and thus

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combined sewer overflows (CSO) (containing raw wastewater, sewer sediment and rain water at the same time) result. . These CSO discharges can be polluted by ng-µg L-1 of organic micro-pollutants (Launay et al., 2016; Ternes, 1998). CSO is expected to contain high BOD loads, as they contain a mix of raw wastewater (high BOD) and rain runoff water (no BOD). The BOD loading will vary very much from catchment to catchment but it will very usually between 100% raw wastewater and 10% raw wastewater. On the other hand stormwater is

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considered to contain very little BOD as it consists of runoff from urban sealed surfaces

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(roads, roofs etc.). Removing micropollutants from CSO by biofilters would increase surface water quality (Dittmer et al., 2016; Flanagan et al., 2019; Janzen et al., 2009). It is imagined,

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that biofilters used for treating CSO and stormwater would in full scale be used as a combined

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treatment/storage facility and would receive 1-4 m of water on top of the biofilter, which

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would then over weeks be percolated through the filter material. Until the next storm event it would only receive the natural rainfall into the filter. – Thus, the conditions in the filter

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systems would change dramatically. Using biofilters for treating CSO would result in pulse

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loading of easily degradable organic carbon (BOD) to these systems. Such high carbon doses couldinfluence the functionality in both hydraulics as well as micropollutant removal(Li et al., 2014). However, with short pulses the overall diversity of the microbial community would remain unchanged, (Li et al., 2013). 1.4 Removing micropollutants in biofilters Organic micropollutants can be removed in biofilters (Andresen and Bester, 2006; Teerlink et al., 2012). Most probably, the effectiveness of the biofilter is dependent on the composition of the microbial biomass, which is on their side mostly dependent on the structure and amount of carbon supply. However, carbon sources can also have other effects:

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a) As the degradation is mostly a co-degradation process (Fischer and Majewsky, 2014), it cannot be excluded that some of the primary substrate also functions as co-substrate for the enzymatic processes, thus there may be a link between concentration of primary substrate and reaction rates (Liang et al., 2019; Zhang et al., 2019) . b) If the loading with carbon exceeds the available, the biofilter can become anaerobic. This would usually result in decreased degradation, as usually aerobic systems are

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more able to degrade micropollutants than anaerobic ones (Massmann et al., 2008;

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Rauch-Williams et al., 2010; Zhang et al., 2019).

c) More carbon supply can induce substrate competition with the degradation of

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micropollutants and thus slow this down (Egli, 2010; Plosz et al., 2010; Zhang et al.,

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2019).

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system.

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d) Higher carbon supply can induce biomass growth and potential clogging of the

Hellauer et al., (2019), Liang et al., (2019), Zhang et al., (2019) and Tang et al., (2017) have

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shown changes in removal of organic micropollutants in different biofilm reactors using both easily degradable carbon sources such as acetate (Liang et al., 2019; Zhang et al., 2019) , and recalcitrant compounds such as humic acid (Tang et al., 2017) in biological systems. All these publications gave interesting insights, however, none of the carbon sources used in these papers can serve as a realistic proxy for CSO. Thus for this study the biofilters were fed with pre-settled raw wastewater since its carbon content is high (chemical oxygen demand (COD); 300-600 mg L-1), and it could mimic conditions present under classical CSO situations. It was thus intended to study effects of wastewater carbon on the removal of organic micropollutants while comparing the effects of four different materials to which biofilms were attached to.

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1.6 Pharmaceuticals Pharmaceuticals are a large group of micro-pollutants that are introduced into receiving waters via treated wastewater (Ternes, 1998). The presence of these compounds in wastewater and CSO originates largely from human excretions as both, parent compounds as well as human metabolites (Daughton and Ternes, 1999). This constant load of pharmaceuticals in wastewater in combination with their persistence, leads to their ubiquitous distribution in the

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environment (Bendz et al., 2005). Low levels of pharmaceuticals have been found in drinking

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water in countries that use surface water as drinking water (Wahlberg et al., 2011). In this

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study, we focus on β-blockers (blood pressure regulators) for mechanistic purposes. βblockers are among the most commonly used drugs - the most frequently used among these in

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Denmark is metoprolol which is among the top 25 sold drugs in Denmark, while other β-

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blockers such as propranolol, atenolol, and sotalol are used less (Sundhedsdatastyrelsen,

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2018). Therefore they can be found in wastewater in concentrations from 10-1500 ng L-1 (Maurer et al., 2007) not only as parent compounds but also metabolites. Known human

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metabolites for metoprolol are, e.g., O-desmethylmetoprolol, α-hydroxymetoprolol, and metoprolol acid, while in microbiological incubations of metoprolol 4-(2-hydroxy-3(isopropylamino)propoxy)phenol (also known as prenalterol) has been detected (Rubirola et al., 2014). Atenolol and sotalol are somewhat more hydrophilic compounds than metoprolol and are metabolized less than the latter compound in humans (Anttila et al., 1976; Reeves et al., 1978). However, atenolol can be transformed into atenolol acid (identical to metoprolol acid) during microbiological treatment (Liang et al., 2019; Svan et al., 2016). Propranolol is degraded by different pathways than metoprolol in humans: One of the phase I human metabolites of propranolol is 4-hydroxypropranolol (Bond, 1967), its sulphate conjugate from human metabolism (phase II) is also known, and can be found in WWTPs (Brown and Wong,

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2016). During conventional activated sludge (CAS) treatment the metabolites of β-blockers can be subject to removal and formation depending on the treatment step (Rubirola et al., 2014). In this study, we investigate the effects of short-term carbon dosing on the removal of 26 pharmaceuticals in established biofilter systems. Consequently, the main objectives were the following: A) to test whether pulse-feeding with pre-settled wastewater (CSO-simulation) can

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alter removal performance; and B) which effects have the changes in carbon supply on the

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first steps of the metabolic chain of β-blocker metabolites: o-desmethylmetoprolol,

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prenalterol, metoprolol acid, α-hydroxymetoprolol, and 4-hydroxypropranolol. Additionally

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we test which influence the four different support materials have on the removal of

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2.1 Chemicals

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2. Materials and methods

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micropollutants.

Analytical standards of thirty-one pharmaceuticals and transformation products were chosen for analysis; four β-blockers (atenolol, metoprolol, propranolol, sotalol) and five of their transformation products (metoprolol acid, α-hydroxymetoprolol, prenalterol, Odesmethylmetoprolol, 4-hydroxypropranolol), four analgesics (diclofenac, ibuprofen, phenazone, tramadol), three antidepressants (carbamazepine, citalopram, venlafaxine), five contrast media (diatrizoic acid, iohexol, iopromide, iomeprol, iopamidol), and ten antibiotics (sulfadiazine (and its human metabolite ac-sulfadiazine), azithromycin, ciprofloxacin, clarithromycin, clindamycin, erythromycin, sulfamethizole, sulfamethoxazole, trimethoprim). Detailed information regarding the studied compounds can be found in supplementary

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material (Table S1). Hydrochloride acid (37%), formic acid, gradient grade methanol, and LC-MS grade water were obtained from Merck (Darmstadt, Germany). 2.2 Biofilters The set-up was similar to Zhang et al., (2019), however, more and different support media as well as different carbon sources were used. The four reactors consisted of a glass column (50 cm length and 2.5 cm inner diameter) from LCTech (Dorfen, Germany) each, and were filled

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with a different support material: A) quartz sand (50-70 mesh, from Sigma Aldrich), B)

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stonewool from ROCKWOOL® (Hedehusene, Denmark), C) Filtralite® CLEAN HR 3-6

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(Leca Norway AS, Norway), and D) quartz sand amended with 1% peat (Baltic Weisstorf,

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Søren Tangaard APS, Denmark) (compare Casas and Bester, 2015). For simplification, these

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materials are nominated “sand”, “stonewool”, “filtralite”, and “1%peat”, respectively. Successive to the packing, biofilm growth was initiated by adding approx. 3 mL of activated

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sludge into the flow path of each reactor, which were otherwise fed by effluent wastewater. Following this initial start, all biofilter systems were adapted to the effluent water conditions

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at least 6 month before the start of the experiments. The biofilters were operated with constant hydraulic flow maintaining a HRT of 16 h in all systems. Due to the different porosity (Table 1), different internal volumes had to be taken into account. Thus, the flow in the biofilters were 0.05, 0.03, 0.09, 0.15 ml min-1 for sand, filtralite, 1%peat, and stonewool, respectively. As basic operation, oxygen saturated effluent water was pumped against gravity through the reactors to remove air bubbles. Filtralite, and peat biofilters were connected to Reglo-CPF digital pumps, and sand, and stonewool biofilters were equipped with MCP-CPP Process pumps, all supplied by Ismatec (Wertheim, Germany). Effluent water and activated sludge was collected at Bjergmarken WWTP (Roskilde, Denmark) and stored at 4°C until use. To avoid system clogging, the effluent water was filtered through a glass-fiber filter

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(Whatman™, GF/D: 2.6 µm) before use. Bjergmarken WWTP is a conventional activated sludge treatment plant, treating mainly municipal wastewater (previously described in Casas and Bester, (2015)). During the experiments no micropollutants were added to the influent of the biofilters – all reported data base on the micropollutants “naturally” present in wastewater. Every 2nd-3rd week all tubes and pumps were cleaned according to a standard operating protocol established by the authors: while the reactor columns were bypassed, the pump-tube

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system was washed successively by a 1% solution of HCl (37%) in deionised water (10 mins),

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methanol (10 mins), tap water (10 mins), and 5-10 mins with effluent water to recondition the

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system. This procedure was necessary to flush out/dissolve calcium carbonate, and remove

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biofilm inevitably accumulating in the tube-system over time.

biofilters

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2.3 Hydraulic characterization of reactors & sorption of pharmaceuticals to support of the

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For all biofilters HRT, sorption of pharmaceuticals and porosity were determined by using the procedure described in Casas and Bester, (2015). Shortly: the individual biofilter was directly connected to an HPLC-UV instrument, NaBr was injected into the inlet of the biofilter, and elution of Br- was detected by the UV-VIS-detector at 215nm. Successively the retention of all pharmaceuticals was determined by individual injection to the reactor column. Every 20 mins a sample from the effluent of the reactor column was automatically taken and injected to an analytical column. Quantitation was performed by UV-vis. All peaks were manually integrated using Chromeleon 7.2 software (Termo Fischer Scientific, USA). Further information can be found in supplementary material (S1, and Fig. S2). The specific surface of the biofilters is shown in Table 1.

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2.4 Pulse-feeding with pre-settled raw wastewater As Bjergmarken does not operate with a primary settler, pre-settled raw wastewater (RWW) was collected at Avedøre WWTP (Hvidovre, Denmark) and stored at -20°C until usage before which it was thawed at room temperature. Avedøre WWTP is a classic conventional sludge WWTP, and operates primary settling tanks. To study effects of the carbon supply, the RWW was mixed with effluent water to reach 10 different concentrations, i.e., 0.2, 0.5, 1, 2, 3, 4, 5,

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10, 15, and 20% RWW in effluent water.

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The pulse-feeding was carried out in intervals of three times the HRT, thus each pulse lasted

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for 48 hours and each pulse with high BOD was placed between two time intervals with pure

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effluent (Fig. S1). Samples were collected over a period from 24 hours until 48 hours after the

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start of each pulse. This long period was necessary to gain enough water volume (40 mL) for the analysis of both COD and pharmaceuticals, and to account for compound specific

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retention time with the different materials (Fig. S2).

2.5 COD and dissolved oxygen

Dissolved oxygen (DO) was measured using a flow-through oxygen sensor with fiber optic (Witrox 4 oxygen meter, Loligo Systems, Viborg, Denmark). The flow-through mini sensor was placed in the flow path before (inlet) or after (outlet) the biofilter. DO and temperature measurements were logged with the WitroxView software. As control experiments with measurements directly at the end of the packing (using a probe (Loligo Systems, Viborg, Denmark) resulted in lower oxygen levels (<0.5 mgO2 L-1) than those obtained by the flowthrough sensor at the end of the outlet, the biofilters were considered (partially) anoxic when the reading of the flow through sensors reached smaller values than 0.5 mgO2 L-1. Probably

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there is a small flow of oxygen through the PEEK tube walls corrupting the measurements of the flow through sensor at the lowest oxygen concentrations. Samples for COD and pharmaceutical analysis were collected from the feed water as well as the outlet of the biofilters during all treatments. COD was analysed according to standard method ISO 15705:2002 (ST-COD) with 1.61 mg L-1 standard error and a limit of detection of 3.11 mg L-1.

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2.6 Quantification of pharmaceuticals

Samples were collected in 12 ml glass vials successively, 1.6 ml of sample was transferred

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into an HPLC-vial and centrifuged for 5 min at 6000 rpm (Z206A, Hermle, Wehingen,

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Germany) to remove particles. 900 µL of supernatant were transferred to a new HPLC-vial

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and 100 µL of methanolic internal standard (IS) solution was added before it was vortexed

until analysis.

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(MS2 Minishaker, IKA®, Staufen, Germany) for ~10 seconds. Samples were stored at -20°C

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Samples were analysed by high-performance liquid chromatography coupled to tandem mass spectrometry (HPLC-MS/MS). Two mass spectrometers were used for detection; an API 4000 (ABSciex, Framingham, MA, USA) was used to analyse all parent pharmaceuticals (described in Casas et al., (2015)), and an API 5500 (ABSciex, Framingham, MA, USA) for the metabolites of the β-blockers (Details are given in Table S2). In brief: the chromatographic separations were performed at 20 °C using a Synergi Polar RP column (150 × 2 mm, 4 µm) (Phenomenex, Torrance, California, USA) with a multi-step gradient elution (see supplementary material Table S3 and S4).The injection volume was 100 µl for all samples analysed on the API 4000, and 40 µL for the samples analysed on the API 5500, respectively. All quantifications were performed using a 6-point calibration

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ranging from 0.05 ng/mL to 10 ng/mL linked to an internal standard (IS) (Table S5) using Analyst 1.6 software (SCIEX, Framingham, MA, USA). Details on limit of quantifications (LOQ), standard deviation from calibration line are provided in supplementary material (Table S5). 2.7 Calculations Succeeding to the experiments, the biological oxygen demand (BOD) of the feed water was

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back calculated from the fraction of a) raw wastewater (which contained a BOD of 293±36

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mg L-1 (n=4)) and b) effluent wastewater (which had a BOD of 3.2±1 mg L-1 (n=24)).

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Removal rate constants (hour-1) were calculated by comparing biofilter outlet (Coutlet) to inlet

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(Cinlet) adjusting to the hydraulic retention time (HRT) and using simple first order reaction

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kinetics as seen in Eq. (1). 𝐶 ln⁡( 𝑜𝑢𝑡𝑙𝑒𝑡 ) 𝐶𝑖𝑛𝑙𝑒𝑡

𝐻𝑅𝑇

(1)

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𝑘=−

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Coutlet, and Cinlet denote the parent compound concentrations (µg L-1) at the outlet and inlet of the biofilters, respectively. k describes single first order removal rate constant, and HRT is the hydraulic retention time during the treatment. Removal was calculated for all metabolites following Eq. (2). 𝑅𝑒𝑚𝑜𝑣𝑎𝑙⁡(%) = (1 −

𝐶𝑜𝑢𝑡 𝐶𝑖𝑛

) ∗ 100

(2)

Formation was calculated as negative removal Eq. (3). 𝐹𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛⁡(%) = (−)𝑅𝑒𝑚𝑜𝑣𝑎𝑙⁡%

(3)

Standard deviation, and relative standard deviation (%) were calculated from injection multiple standards at a concentration of: 1 µg L-1 (parents), and 0.3 µg L-1 (transformation 13

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products). Details can be found in supplementary materials Table S6. These relative standard deviations were used as error bars in Figure1-6.

3. Results and discussion 3.1 Materials inside the biofilter

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All biofilters were characterized hydraulically (Figures and Tables

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Table 1). Also for all compounds elution profiles were determined (supplementary Fig. S2) to

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be sure to link the right outflow fraction to the respective inflow. As can be seen, in Fig. S2,

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the maximum of the elution occurs at 70 mL. The compounds are quantitatively eluted after

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150 mL. Thus after continuous dosing of 150 mL influent, the measured concentration differences outflow vs. inflow should be determined by degradation, not sorption.

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Based on the surface to volume ratio (available surface per reactor), the filtralite biofilter has a 50-times higher calculated surface area than the sand biofilter, which theoretically should

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provide more space for microbial growth on the filtralite material. However, a part of the filtralite surfaces are inner surfaces which may not easily be reached by the microorganisms. 3.2 Oxygen and COD consumption The inlet water was oxygen-saturated over the experimental period (8.5±0.6 mgO2 L-1 (20°C), n=15). Oxygen was in all cases > 0.5 mgO2 L-1 in the outlet tube, which indicates predominantly aerobic conditions inside the biofilters (Figure 1). COD-consumption increased parallel to the RWW fraction added (Figure 2). These results points toward a system, that is over vast areas not limited by oxygen but by available carbon as hypothesized in the introduction.

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There is, however, a disagreement between COD removal, and oxygen consumption, as the removed COD (Figure 2) should be more than enough to consume all O2 in the system, assuming mineralisation, while the systems is still measured as aerobic. Previous experiments by Zhang et al., (2019) have also indicated that the theoretically calculated consumption of carbon does not compute with actual data - rather it shows more carbon can be consumed than predicted, hence we assume that incorporation of carbon from the feed solution into the

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biomass, takes place, which is as such not determined. An attempt to estimate biomass yield

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was performed on the basis of stoichiometry of aerobic oxidation of acetate by heterotrophic bacteria. Following Metcalf and Eddy (2003), biomass relates to COD like 1 g cells equal

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1.42 g COD. A loss of 36 mg COD L-1 relates thus to 25 mg cells. As the sand column was

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pumped with 0.05 mL min-1 (72 mL d-1) a removal of 36 mg L-1 COD would result in a COD

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mass loss of 2.5 mg d-1. This would equal to a growth of 1.8 mg biomass d-1 during maximum BOD loading. This simple calculation can help explain the fate of the remaining COD.

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the system dramatically.

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However, these low growth rates over limited time periods (24 hours) cannot really change

3.3 Removal of pharmaceuticals

The removal of pharmaceuticals is presented as single first order removal rate constants (h-1) for the detected indigenous inlet concentrations of atenolol, metoprolol, propranolol, iohexol, iomeprol, trimethoprim, sulfamethizole, sulfamethoxazole, venlafaxine, citalopram, carbamazepine, diclofenac, and partially ibuprofen (Figure 5, 6 and 7, and Fig. S3, and S4). During periods with no addition of raw wastewater, almost all pharmaceuticals (except sulfamethoxazole) were removed to some extent in the biofilters. In the following the effect of addition of pre-settled raw wastewater (BOD dosing) on pharmaceuticals removal rate constants are evaluated (Table S7).

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3.3.1 Compounds influenced by BOD dosing The removal rate constants of atenolol, metoprolol, propranolol, iohexol, diclofenac, citalopram, and venlafaxine were affected by the increasing BOD concentrations. This is especially visible considering the filtralite biofilter. However, the different effects are discussed in the following paragraphs ordered by compound group:

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β-blockers

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Metoprolol, atenolol, sotalol and propranolol were detected with 2.0, 0.12, <0.05, 0.12 µg L-1, respectively, in the inflow of the biofilter systems, as typical and expected in the Danish

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context. In three filter systems, the removal rate constants of the three detectable β-blockers

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increased with increasing BOD concentration up to a maximum, which can be quite different

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for the different compounds and filters (Figure 3, and Fig. S3): For metoprolol, the removal rate constant in the sand biofilter increased from 0.04 to 0.14 h-1

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(at BOD concentration 47 mg L-1). At this BOD loading a maximum of the removal rate is experienced: at higher BOD the metoprolol reaction rate constant decreased to 0.005 hour-1,

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which was below initial rate constant of 0.085 hour-1 (Figure 3). A similar pattern was observed for the filtralite biofilter for which a maximum was reached at a lower BOD concentration (12 mg L-1). The 1% peat based biofilter and the stonewool biofilter exhibited opposite trends, and minimum removal rate constants between BOD concentrations of 6-18 mg L-1, were observed. It is possible that the increasing degradation rates with BOD can be interpreted as: i) metoprolol is degraded in a co-degradation process and ii) with higher availability of the primary substrate, the reaction rates with metoprolol are increased. Decreasing reaction rates at higher BOD can be induced by reduced availability of oxygen or

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rising competition of other compounds on the active sites of the degrading enzymes. BOD dependent removal maxima have been observed and discussed by Zhang et al., (2019) before. For propranolol and atenolol removal rate constants had highest values (0.2, and 0.3 h-1) at effluent BOD concentrations (3.5 mg L-1) in the filtralite based system. However a second maximum of 0.15 and 0.2 were observed at BOD=10 and 25 for these two compounds. The stonewool and peat amended systems had highest removal rate constants for these two β-

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blockers below 9.1 mg L-1 BOD (Fig. S3).

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It is thus interesting to see that not only BOD load (similar to Zhang et al., (2019)) had an effect but also the support medium of the biofilms. Obviously, the surface to volume ratio in

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the biofilter systems should have effects, and that explains why the reaction rate constants for

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the Stonewool material are comparatively low. However, why this moves the maxima in these

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functions, the way it does, is a bit more difficult to understand. Bertelkamp et al., (2016) observed a removal rate constant of up to 0.12 h-1 for metoprolol

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during a “shock-load” of 2 µg L-1 organic micropollutants (including metoprolol) in a soil

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column (riverbank filtration simulation) fed with river water (dissolved organic carbon; 4 mg L-1). The results of these authors are similar to ours at a BOD concentration of 47 mg L-1 albeit the organic carbon (organic micropollutants versus raw wastewater) fraction added are different. On the other hand, Zhang et al. (2019) detected changes in removal rate constant for atenolol, propranolol, and metoprolol with increasing dosage of acetate in a sand biofilter system where all compounds reach maximum removal rate constants around 50 mg C L-1, which is higher than our maximum in the sand filter column (47 mg L-1 BOD (equaling about 20 mg C L-1) this is probably caused by the higher diversity in structures of the C sources in our experiment).

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Metabolites of β-blockers During the experiments, the following metoprolol metabolites were quantified: O-desmethylmetoprolol, metoprolol acid, and α-hydroxymetoprolol. All these metabolites were already present in the inlet of the biofilters (originating from either human or sludge metabolism) at detectable concentration as expected (Rubirola et al., 2014). O-desmethylmetoprolol was present in feed water at concentration 0.06±0.01 µg L-1, and

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removal and formation are presented in Figure 4. In the sand and 1% peat biofilters the

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concentration in the effluent of the biofilters were higher than in the inflow and thus the

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metabolite was formed especially at low BOD. This supports the theory on biodegradation in these systems. However, this metabolite was removed in the stonewool and filtralite biofilter

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systems, indicating towards these systems having a different metabolic functioning than the

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two sand based systems, as already discussed in the section on metoprolol. Odesmethylmetoprolol is an intermediate when metoprolol is metabolised to metoprolol acid in

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the human metabolism. Therefore, a low formation of O-desmethylmetoprolol could indicate

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metoprolol is transformed by other pathways in the stonewool and filtralite biofilters. Metoprolol acid inlet (biofilter) concentrations were 0.6±0.2 µg L-1. The removal of metoprolol acid is shown in Fig. S5. For metoprolol acid, an overall removal was basically observed during all treatments in all four biofilter systems. α-hydroxymetoprolol is present at inlet concentrations 0.13±0.7 µg L-1, and it is always removed in the biofilters reaching concentrations below LOQ (<0.05 µg L-1) at the outlet (Fig. S5). Thus, two scenarios are possible; 1) the oxidative transformation leading to αhydroxymetoprolol is not happening in these biofilter systems, 2) it is formed and the metabolite is too rapidly further degraded. Rubirola et al., (2014) found less than 5% formation of α-hydroxymetoprolol from metoprolol during a spiked sludge incubation

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experiment. Theoretically, this would provide a formation concentration of 0.1 µg L-1 in our experiment, which would have been detectable. X ray-contrast media – iohexol In Figure 5, the removal rate constants for iohexol against increasing BOD concentrations are plotted, and for the filtralite based biofilter an exponential function was fitted to the data (yellow line). Similar trends were detected for the sand, stonewool, and peat based biofilters

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though the data was more scattered and thus no fit was attempted. Iohexol has been

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characterized by Joss et al. (2006) as partially removable in conventionally wastewater

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treatment. However, it can be removed almost quantitatively in this experiment over the

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whole range of BOD. The highest removal rate constants was observed for iohexol in the sand biofilter, 0.4 h -1, and in the filtralite biofilter, 0.7 h-1 both at BOD loading of 3.5 mg L-1

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whereas in the peat biofilter it was 0.3 h-1 at BOD loading of 9 mg L-1. These removal rates

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constants are close to 3 times or higher than results, obtained by Casas and Bester (2015) for a similar sand biofilter (i.e., 0.1 h-1). Zhang et al. (2019) found a reaction rate constant of 0.1 h-1

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during increasing acetate dosing in a sand biofilter system. These biofilters were identical to ours, thus our results points towards stimulating removal of media in biofilms by BOD loadings is possible. Data for iomeprol differed to those for iohexol as removal rate constants for the latter compound decreased with increasing BOD (Fig. S4), a pattern also observed by Zhang et al. (2019). Diclofenac, citalopram, and venlafaxine In all four biofilters, diclofenac, citalopram, and venlafaxine removal rate constants peak at BOD concentrations below 15 mg L-1, giving removal rate constants of: 0.1 (filtralite), 0.2 (peat), 0.07 h-1 (filtralite), respectively (Figure 6, and Fig. S3). In the dataset of Casas and

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Bester (2015), removal of diclofenac was strongly positively correlated (R2=0.93) to HRT in sand biofilters, and the authors observed no removal at 4 h, and 82% at 35 h. Hellauer et al. (2019) noted quantitative removal of diclofenac during biofiltration of drinking water with low addition of humic acid, at a HRT of (21 h). These results are in line with our sand biofilter having a HRT of ~16 h. For the other compounds there is little comparable data. 3.3.2 Compounds less or not influenced by BOD dosing

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Compounds such as sulfamethoxazole, and carbamazepine that are classified as recalicitrant

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in CAS systems (Joss et al., 2006) show little but significant removal in the biofilters. Their

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removal rate constants (0.01-0.1 h-1) are higher in the biofilters with slightly elevated BOD

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concentrations even though the effect of BOD is very low (Figure 7, and Fig. S4). Removal

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for carbamazepine is especially visible in the filtralite system, while the other systems seem to be less able to remove this compound. In the filtralite biofilter system, a maximum removal,

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0.1 h-1 is visible around 10 mg L-1 BOD. For sulfamethoxazole the highest removal rate constants (0.05 h-1) were determined in the filtralite biofilter at a BOD load of 47 mg L-1.

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However, the overall removal of sulfamethoxazole under the selected conditions is below 50%. During treatments of, e.g., sulfamethoxazole negative removal was observed in single cases. This may be due to microbial deconjugation of human metabolites (such as acetyl sulfamethoxazole) within the biofiltration systems – these reactions have been discussed extensively in the literature, e.g., recently by Polesel et al. (2016). Similar “formation” is observed for sulfamethizole (Fig. S4). Considering the removal of iomeprol, the removal rate constants are usually around 0.25 h-1 and they not change significantly under different BOD dosages. However, one removal rate constant, 1.1 h-1 (filtralite) is almost three times higher than all others, and could be considered an outlier (Fig. S4)

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3.3.3 Decrease of degradation due to high BOD loading Ibuprofen, which is normally rapidly removed during CAS and present in effluent at less than 0.5 µg L-1 (Ternes, 1998), entered the systems together with the presettled raw wastewater at concentrations up to 5 µg L-1 at BOD loadings of 62 mg L-1 (Fig. S6). Therefore, removal of ibuprofen was quantified at BOD concentrations >32 mg L-1. It was observed that up to 50% of ibuprofen was removed in the filtralite based biofilter, whereas in the 1%peat and sand

).

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1

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based biofilters more than 80% ibuprofen were removed (removal rate constants around 0.1 h-

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3.4 Biofilter material

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Biofilter materials are known to be affected by biofilm accumulation over time, which can

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result in changes in HRT. In this experiment sand, 1%peat, and stonewool were affected by the increasing concentrations of RWW which possibly gave slight growth of biomass. It

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resulted in adaption in the pump settings to maintain flow rate, and adjustments during the

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treatments were necessary. However, this was not observed for the filtralite biofilter system, indicating its potential as filter material. 3.5 Size of potential biofilters for polishing effluent waters from small treatment plants. As discussed in Casas and Bester (2015), good results can be expected for biofilters operating at 16-20 h HRT. Our data show, that this is feasible even if the CAS plant upfront is operating somewhat unstable. The size of the plant will depend on how deep a biofilter can be utilized. Considering a 100 PE plant about 13 m3 hour-1 would be considered as dry weather flow. During rain-events usually 36 m3 hour-1 would be considered. With 20 h HRT this would add up to a biofilter volume of 720 m3. If only an active layer of 0.2 m can be used the resulting area use would be 3600 m2. However, most probably a filter depth of 2 m can be used with a

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resulting area of 360 m2. - These numbers might be acceptable for small rural treatment plants. Whether 2 m depth can be used needs to be tested in piloting experiments.

4. Conclusion Biofilters can remove pharmaceuticals from polluted waters, such as effluents of small

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WWTPs and treatment of stormwater and CSO seems feasible. This holds even with elevated

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loadings of BOD in all four tested systems: the sand, stonewool, 1%peat and filtralite based biofilters – though vast differences in efficiencies were observed. The removal of

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micropollutants in the biofilters were dependent on the BOD loading: some compounds

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degraded faster with higher loading, others slower. However, there is no indication towards a

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stop of the biodegradation processes even at high BOD loadings. Retention times of 10-30 h

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should be considered for reliable removal of 90% of the pharmaceuticals load. Considering the observed compounds: atenolol, propranolol, diclofenac, metoprolol,

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venlafaxine, citalopram, and iohexol were affected by the pulsed BOD loading. The effects were also dependent on the support medium. However, in general terms it can be concluded that the degradation of the β-blockers metoprolol, propranolol and atenolol were positively influenced by increasing the BOD, their removal had a maximum at BOD 47 mg L-1 Higher BOD values had negative effects. The degradation of diclofenac, venlafaxine and citalopram, was also positively affected by BOD however the maximum was observed at lower BOD values of 15 mg/L. The degradation of iohexol was negatively influenced by increasing BOD. The degradation of sulfamethizole, sulfamethoxazole, trimethoprim, iomeprol, and carbamazepine were not influenced by BOD loading.

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Under the same conditions, the biofilters based on different porous materials produced differences in removal, not only kinetically but also mechanistically (i.e., there are indications towards different metabolic pathways are relevant in the different biofilters ). For most compounds the biofilters built with materials with high inner surfaces (filtralite) performed best in degrading compounds. However, looking at the data from an another angle: even though differences were observed

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that should be taken into account when designing biofilters, it should be mentioned that the

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effects by these singular pulsed changes were limited and were usually ranging below x 10,

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indicating towards there is a fair chance this technology will give performance even under

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Acknowledgements

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challenging conditions such as treatment of CSO.

This study received funding via the BONUS CLEANWATER project which has received

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funding from BONUS (Art 185), funded jointly by the EU and Innovation Fund Denmark, Sweden's innovation agency VINNOVA and the German Ministry for Education and Science (BMBF). The authors wish to acknowledge; Annegret Budach, and Sidra Ilyas for their contribution to biofilter set-up and hydraulic characterization, and LECA Norway AS, and ROCKWOOL® for providing materials.

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Bollmann, U.E., Vollertsen, J., Carmeliet, J., Bester, K., 2014. Dynamics of biocide emissions from buildings in a suburban stormwater catchment - Concentrations, mass loads and emission processes.pdf. Water Res. 56, 66–76. Bond, P.A., 1967. Metabolism of Propranolol ('lnderal’*), a Potent, Specific [3-Adrenergic Receptor Blocking Agent. Nature 1967. Brix, H., Arias, C.A., 2005. The use of vertical flow constructed wetlands for on-site treatment of domestic wastewater: New Danish guidelines. Ecol. Eng. 25, 491–500. https://doi.org/10.1016/j.ecoleng.2005.07.009 Brown, A.K., Wong, C.S., 2016. Simultaneous quantification of propranolol and sulfamethoxazole and major human metabolite conjugates 4-hydroxy-propranolol sulfate and sulfamethoxazole-β-glucuronide in municipal wastewater—A framework for multiple classes of drugs and conjugates. J. Chromatogr. A 1471, 34–44. https://doi.org/10.1016/j.chroma.2016.10.011 Carpenter, C.M.G., Helbling, D.E., 2017. Removal of micropollutants in biofilters: Hydrodynamic effects on biofilm assembly and functioning. Water Res. 120, 211–221. https://doi.org/10.1016/j.watres.2017.04.071 Casas, M.E., Bester, K., 2015. Can those organic micro-pollutants that are recalcitrant in

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Wahlberg, C., Björlenius, B., Paxéus, N., 2011. Fluxes of 13 selected pharmaceuticals in the water cycle of Stockholm, Sweden. Water Sci. Technol. 63, 1772–1780. https://doi.org/10.2166/wst.2011.124

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Figures and Tables Table 1 Properties of the four different materials used to construct the biofilters, and measured individual biofilter void volume, and porosity. Material

Type

Particle size [mm]

Specific surface area 2

Available surface area for biofilms in the respective 30 mL column

-1

[m g ]

Void volume [ml]

Porosity [%]

Reference

53.9

36

77.3

45

78,319

160

91

This study, ROCKWOOL®

63,968

78.3

48

This study

2

Fiber material

0.2710.283

0.2100.297

0.0144; 0.0257

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3,150,000

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99% Sand amended with 1% Peat

Stonewool (heated basalt rock) Decayed organic matter+quartz sand

1.8-2.9

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Expanded clay particles

0.0257

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Filtralite Clean HR 3-6 Stonewool

0.2100.297 2.5-6

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Quartz sand

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Sand

[cm ] 64,250

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Casas and Bester, (2015) This study, LECA®

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Table 2 Water quality and pharmaceutical concentrations of the two WWTPs. N/A; there is no concentration data for pharmaceuticals at Avedøre WWTP since only presettled raw wastewater was used, and in the end mixed with effluent from Bjergmarken. Bjergmarken effluent WWTP

Avedøre presettled raw wastewater WWTP

36±12*

461±32**

BOD [mg L-1]

3.2±1*

293±36**

Metropolol [µg L-1]

1.9±0.3

N/A***

Iohexol [µg L-1]

3.9±2.1

N/A***

Diclofenac [µg L-1]

0.5±0.1

Ibuprofen [µg L-1]

0.2±0.1

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COD [mg L-1]

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*2018 average **Data from two sampling days

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*** same range as Bjergmarken effluent

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N/A*** N/A***

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8 Filtralite 6 4 2

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DO concentration [mg O2 L-1]

10

0 20

40

60

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0

-1

-p

BOD [mg L ]

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Figure 1 Dissolved oxygen (DO) concentration in the outlet of the Filtralite biofilter at increasing BOD concentrations (0, 3.2, 3.5, 4.7, 6.1, 9.1, 12, 14.9, 17.8, 32.5, 47.1, 61.8 mg L1 added to the CAS effluent values). The 0-value represents the inlet (100% CAS effluent water).

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COD removal [%]

100

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Stonewool 1%Peat 0

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Figure 2 Chemical oxygen demand (COD) removal at increasing BOD concentration (3.2, 3.5, 4.7, 6.1, 9.1, 12, 14.9, 17.8, 32.5, 47.1, 61.8 mg L-1) for the four different biofilters. The fitted lines follows segmental linear regression divided at BOD 20 mg L-1 to illustrate the two trends observed. The error bars were set to 1.6% for all data points.

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Sand

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Filtralite 0.20

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0.05 0.00 0

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Figure 3 Removal rate constant of metoprolol against BOD concentration for the four different biofilter materials. The fitted lines (yellow and black) for filtralite, and sand are plotted as segmental linear regression, and X0 was set to 47 mg L-1. Asterisks next to points represents values were LOQ was used as Coutlet. Calculations for error bars can be found in supplementary material Table S6.

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Filtralite

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Stonewool 1%Peat

-60 0

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Figure 4 Removal/formation of O-desmethylmetoprolol against BOD concentration for the four different biofilter materials. Asterisks next to points represents values were LOQ was used as Coutlet. Error bars were set to 6.6% based on calculations presented in supplementary materials Table S6.

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Sand 0.8

Filtralite *

Stonewool

0.6

1%Peat 0.4 *

* * * *

* *

0.2 ** * * ** * *

0.0 0

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* * * *

* * * *

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* *

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Figure 5 Removal rate constant of iohexol against BOD concentration for the four different biofilter materials. The fitted line (yellow) for filtralite is plotted as exponential function. Asterisks next to points represent effluent values so low, that the LOQ was used as Coutlet Calculations for error bars can be found in supplementary material Table S6.

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Sand

0.12

Filtralite Stonewool 0.08

*

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0.04

0.00

0

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Figure 6 Removal rate constant of diclofenac against BOD concentration for the four different biofilter materials. Asterisks next to points represents values were LOQ was used as Coutlet. Calculations for error bars can be found in supplementary material Table S6.

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*

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BOD [mg L ]

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Figure 7 Removal rate constant of sulfamethoxazole against BOD concentration for the four different biofilter materials. Asterisks next to points represents values were LOQ was used as Coutlet. Calculations for error bars can be found in supplementary material Table S6.

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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.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Graphical abstract

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Short pulses of BOD influenced the removal rate constants. While the removal of some compounds was enhanced, those for others was decreased. Filtralite and sand based biofilters had highest removal for micropollutants.

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