Assessing the cumulative pressure of micropollutants in Swedish wastewater effluents and recipient water systems
using integrated toxicological and chemical methods
Kemiska och biologiska analyser i anslutning till
reningsverk for att följa upp de beräkningar som gjordes i regeringsuppdraget om avancerad rening
Oksana Golovko
1, Johan Lundqvist
2, Stefan Örn
2, Lutz Ahrens
11Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
2Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
Rapport till Naturvårdsverket
Department of Aquatic Sciences and Assessment
2 Överenskommelse NV-03301-18
Uppsala, 2020-04-23
3
Assessing the cumulative pressure of micropollutants in Swedish wastewater effluents and recipient water systems using integrated toxicological and chemical methods
Kemiska och biologiska analyser i anslutning till reningsverk för att följa upp de beräkningar som gjordes i regeringsuppdraget om avancerad rening
Rapportförfattare Oksana Golovko, SLU Stefan Örn, SLU Johan Lundqvist, SLU Lutz Ahrens, SLU
Utgivare
Institutionen för vatten och miljö (IVM) Sveriges lantbruksuniversitet (SLU) Postadress
Box 7050, 750 07 Uppsala Telefon
018-671000 Rapporttitel och undertitel
Kemiska och biologiska analyser i anslutning till reningsverk for att följa upp de beräkningar som gjordes i regeringsuppdraget om avancerad rening
Beställare Naturvårdsverket 106 48 Stockholm Finansiering Nationell MÖ Nyckelord för plats
Screening, sewage treatment plant, wastewater, recipient Nyckelord för ämne
Organic micropollutants
Tidpunkt för insamling av underlagsdata 2018-10-31 – 2019-12-31
Sammanfattning
De senaste åren har ett omfattande arbete lagts ner på att bestämma förekomst, öde, spridning och effekter av organiska mikroföroreningar (OMF) i den akvatiska miljön. Ofullständig borttagning av OMF har observerats i konventionella avloppsreningsverk och OMF har även observerats i ytvatten över hela världen över. I denna studie analyserades inkommande och utgående avloppsvatten, avloppsslam samt ytvatten uppströms och nedströms avloppsreningsverk för totalt 225 organiska mikroföroreningar bestående av läkemedel, hormoner, kroppsvårdsprodukter, industrikemikalier, PFAS-ämnen och pesticider. Urvalet av OMF baserades på årlig användning av ett brett spektrum av hushållsprodukter samt oron för deras potentiella effekter hos människor och akvatiska organismer. Utöver detta utfördes 11 olika typer av toxicitetstoxiska bioassays och fiskembryotester på det inkommande och utgående avloppsvattnet samt uppströms och nedströms i ytvattnet hos avloppsreningsverkens recipienter.
NATIONELL
MILJÖÖVERVAKNING PÅUPPDRAGAV NATURVÅRDSVERKET
ÄRENDENNUMMER AVTALSNUMMER PROGRAMOMRÅDE DELPROGRAM
NV-03301-18 219-18-007 Miljöggiftsamordning Screening
4
Totalt detekterades 158 kontaminerande ämnen i åtminstone ett prov, medelkoncentrationerna varierade mellan ng/l till mg/l i avloppsvattenproverna och mellan ng/l till µg/l i ytvattenproverna. Två industrikemikalier (tetraethyleneglycol, laureth-5 and di-(2-ethylhexyl)phosphoric acid), 15 läkemedel (salicylsyra, diklofenak, losartan, valsartan, venlafaxin, oxazepam, lamotrigine, karbamazepin, tramadol, hydroklortiazid, furosemid, ranitidin, bikalutamid och metformin) och stimulanterna kaffein och nikotin representerade 70 % av den kombinerade koncentrationen av föroreningar i inkommande och utgående avloppsvatten samt ytvatten. Av de 225 målföroreningarna organiska mikroföroreningarna kunde 104 detekteras i avloppsslamsproverna. Analys av slammet avslöjade stora koncentrationsvariationer mellan de olika reningsverken, vilket kan förklaras av olikheter i det inkommande avloppsvattnets föroreningssammansättning samt verkens olika reningssteg.
I cellbaserade toxicitetstester i provrör avslöjades hög aktivitet hos de studerade slutpunkterna parametrarna i det inkommande avloppsvattnet. Reningseffektiviteten varierade mellan toxicitetsparametrarna men även mellan reningsverken. För östrogena och androgena aktiviteter var reningseffektiviteten genomgående hög (97—99 %).
Reningseffektiviteten varierade mellan 60— och 90 % för aktiviteten av AhR och oxidativ stress i de olika reningsverken.
Resultaten från denna studie visade att de höga koncentrationerna av OMF i reningsverken och avloppsslammet överförs till akvatiska miljöer. Det finns emellertid begränsad information om reducering av mikroföroreningar i avloppsreningsverk. Avancerad teknik såsom membranfiltrering, koladsorptrion och AOP (advanced oxidative processes) anpassas för avlägsnandet av OMF men prestationen effektiviteten och kostnaden varierar för varje enskilt avloppsreningsverk. Följaktligen bör effektiviteten av reningsmetoderna och processernas stabilitet i reningsverken utvärderas för att kunna säkerställa att effekter och koncentrationer av OMF reduceras. Resultaten kan tillhandahålla en teoretisk bas för att optimera olika typer av befintliga reningssystem och kan bidra till ett signifikant skydd av vatten i recipienter.
5
Summary
In recent decades, a substantial amount of work has been conducted to determine the occurrence, fate, and effects of organic micropollutants (OMPs) in aquatic environments. Incomplete removal of OMPs by conventional wastewater treatment plants (WWTPs) has been observed, and OMPs have been detected in surface water worldwide. In this study, WWTP influent, effluent, and sludge, and upstream and downstream waters in WWTP recipients, were analyzed for a total of 225 OMPs, including pharmaceuticals, hormones, personal care products, industrial chemicals, PFASs, and pesticides. The OMPs were selected based on annual usage in a wide range of household products and concerns about possible effects on humans and aquatic organisms. In addition, 11 different types of toxicity bioassays and fish embryo toxicity tests were applied to WWTP influent and effluent, and upstream and downstream waters in WWTP recipients.
A total of 158 contaminants were detected in at least one sample, in mean concentrations ranging from ng/L to mg/L in wastewater samples and from ng/L to µg/L in surface water samples. Two industrial chemicals (tetraethyleneglycol, laureth-5 and di-(2-ethylhexyl)phosphoric acid), 15 pharmaceuticals (salicylic acid, diclofenac, losartan, valsartan, venlafaxine, oxazepam, lamotrigine, carbamazepine, tramadol, hydrochlorothiazide, theophyline, furosemide, ranitidine, bicalutamide, and metformin), and the stimulants caffeine and nicotine were responsible for 70%
of the combined concentration of pollutants in WWTP influent and effluent, and in surface water.
Of the 225 target OMPs, 104 were detected in sludge samples. Analysis of sludge revealed large variations in concentrations between individual WWTPs, which can be explained by differences in OMP composition in influent water and in operating conditions at WWTPs.
In vitro toxicity testing showed relatively high activities for the studied endpoints in influent waters. The removal efficiency in WWTPs varied between endpoint and plant. For estrogenic and androgenic activities, the removal efficiency was consistently high (97-99%). For AhR activity and oxidative stress, the removal efficiency ranged from 60 to 99% in different WWTPs.
The results revealed that the high concentrations of OMPs in WWTPs and sludge are introduced to aquatic environments. However, there is limited information about the removal mechanisms of OMPs in WWTPs. Advanced technologies, namely membrane filtration, carbon adsorption, and AOPs (advanced oxidative processes), are now being widely adopted for OMPs removal, but the performance and cost of different unit processes vary by case. Therefore, the influence of treatment
6 performance and process stability in WWTPs on reducing the effects and concentrations of OMPs should be evaluated. The results could provide a theoretical basis for optimization of existing treatment systems of different designs, and could contribute significantly to protecting recipient waters.
7
1. Introduction
Contamination of aquatic environments by organic micropollutants (OMPs) such as pharmaceuticals, personal care products, and hormones has raised strong concerns due to their potential bioaccumulative and toxic characteristics [1-3]. OMPs such as pharmaceuticals are continually being released into sewer systems as unaltered parent compounds or metabolites.
Previous studies have found that the removal of OMPs by wastewater treatment plants (WWTPs) is not sufficient [1, 4]. It is estimated that 70-80% of the OMPs in the aquatic environment are released by WWTPs, while the remaining 20-30% originate from other sources of pollution (e.g., livestock and industrial wastes, improper or illegal disposal of unused or expired pharmaceuticals) [2, 5]. As a consequence, OMPs have been ubiquitously found in groundwater, surface water, drinking water, and municipal sewage sludge across the globe [5-8]. Identification and determination of OMPs and their transformation products at environmentally relevant levels are important to understand their metabolism, excretion patterns, dispersion, mobility, and persistence under environmental conditions (biotic and abiotic degradability).
It has repeatedly been reported that analyzed chemicals only explain a relatively small fraction of the observed bioactivities in environmental water samples, highlighting the need for effect-based approaches to fully understand the presence of known and unknown chemicals in the aquatic environment and to assess the removal efficiency of chemical pollutants during wastewater treatment. Toxicological effects measured in vivo and in vitro are valuable tools in such an effect- based approach.
In 2017, the Swedish Environmental Protection Agency (SEPA) reported the results of a government assignment to investigate the need for advanced treatment at Swedish WWTPs [9].
Based on measured levels of mainly pharmaceuticals at certain WWTPs and data on person- equivalents connected and flow rates in recipients, calculations of expected levels in recipient waters were performed and an assessment was made of whether the levels posed any risk to the environment. The calculations showed that in some cases, the levels of certain pharmaceuticals can be expected to exceed environmental quality standards, indicating a risk to the recipient.
Therefore advanced treatment may be needed at some locations. As expected, the risk of negative effects was found to be highest in recipients with low (or varied) water flows, or recipients receiving wastewater from several or very large WWTPs. The substances estimated to have the
8 highest risk ratios were ethinylestradiol, estradiol, levonorgestrel, diclofenac, ibuprofen, metoprolol, and PFOS.
The overall aim of the present study was to assess the impact of WWTPs on aquatic environments using chemical and toxicological characterization, with the specific aim of investigating the agreement between calculated risk ratios of the above-mentioned specific pollutants in the recipients and risk ratios based on measured values. WWTP influent, effluent, and sludge and upstream and downstream waters in the WTTP recipient were analyzed for a total of 225 OMPs.
The hormones (n = 12) were only analysed in ten selected wastewater samples. Ibuprofen and 17α- ethinylestradiol were not included in the analysis, due to limitations of the analytical methods. The OMPs were selected based on their annual usage in a wide range of household products and concerns about their possible effects on humans and aquatic organisms.
In addition, 11 different types of bioassays, based on cultured mammalian cells modified to respond to important classes of environmental pollutants, and fish embryo toxicity tests were applied to WWTP influent and effluent, and to upstream and downstream waters from the WWTP recipient.
Specific objectives were to determine:
i) Occurrence of OMPs in WWTPs, sludge and recipient water (upstream and downstream of WWTPs).
ii) Presence of bioactive chemical pollutants in surface waters (upstream and downstream of WWTPs), using effect-based methods.
iii) The treatment efficiency of WWTPs and release of OMPs.
iv) The treatment efficiency of WWTPs for bioactive chemical pollutants, using effect-based methods.
v) The impact of WWTPs on recipient waters in terms of chemical and toxicological risks.
9
2. Material and methods
2.1 Target OMPs and chemicals 2.1.1 Target OMPs
All analytical standards used for analysis were of high purity grade (>95%). Native standards (n=225) were acquired from Sigma-Aldrich (Sweden). Isotopically labeled standards (IS) (n=30) for the target compounds were obtained from Wellington Laboratories (Canada), Teknolab AB (Kungsbacka, Sweden), Sigma-Aldrich, and Toronto Research Chemicals (Toronto, Canada).
Detailed information about internal standards (IS) and native standards can be found elsewhere [1, 4].
2.1.2 Chemicals for chemical analysis
Ultrapure water was generated by a Milli-Q (MQ) Advantage Ultrapure Water purification system and filtered through a 0.22 µm Millipak Express membrane and an LC-Pak polishing unit (Merk Millipore, Billercia, MA). Methanol, acetonitrile, formic acid and ammonia of high analytical grade were acquired from Sigma-Aldrich (Sweden).
2.2 Sampling
Wastewater samples (influent and effluent) were collected as 24-h or one-week composite samples from 28 WWTPs in Sweden during June 2018. Detailed information is given in Table SM1 in Supporting Materials (SM). The WWTPs and recipients were chosen based on the report published by SEPA [9], and the other sites were selected based on population density and flow conditions.
Surface water samples (0.1 m below the water surface) were collected in the recipient of WWTPs (if possible both downstream and upstream of the WWTP effluent entry point), as grab samples taken during June 2018. All samples were collected into 1-L high-density polyethylene bottles, immediately frozen, and stored frozen (-20°C) until analysis. Sludge samples were collected from dewatered sludge at 24 WWTPs (Table SM1).
10 2.3 Sample preparation
2.3.1 Chemical analysis
Procedures for preparation of water and sludge samples for instrument analysis were as described in detail previously [10, 11]. Briefly, for the two-dimensional liquid chromatography (LC/LC) method, thawed wastewater and river water samples (10 mL aliquots) were filtered through a syringe filter (0.45 μm, regenerated cellulose; VWR, Sweden). All water samples were spiked with the IS mixture to achieve a concentration of 50 ng/L.
The sludge samples were prepared using an ultrasonic-based solvent approach, for which detailed information can be found elsewhere [11]. Briefly, the sludge samples were air-dried overnight in a clean fume hood. Before extraction, a IS mixture (c = 10 ng/g dw sludge) was added to 2 g dry sludge sample. Then 4 mL of acetonitrile and water (1/1, v/v, 0.1% formic acid) were added to the air-dried sludge and the samples were ultrasonicated for 15 min. The supernatant was filtered through a syringe filter (0.45 μm, regenerated cellulose, VWR, Sweden) into 10-mL vials. The step was repeated with a second extraction solvent mixture (acetonitrile, 2-propanol, and water (3/3/4 v/v/v with 0.1% formic acid)). The two supernatants were combined, mixed well, and 1 mL of the extract was used for analysis.
The wastewater samples for hormone analysis were prepared by using solid phase extraction (SPE) as described elsewhere [1]. Briefly, all samples were filtered using glass fiber filters (GFF, 0.45 µm, Whatman, GE Healthcare, IL, USA). An aliquot of 500 mL for each wastewater sample were transferred to a pre-rinsed (methanol) 1 L PP bottle. Each sample was spiked with 20 ng of the hormones IS mixture per aliquot of sample. For the SPE, 200 mg HLB cartridges (Waters Oasis, MA, USA) were used for the wastewater samples, all pre-conditioned with 6 mL methanol followed by 6 mL Millipore water by gravity. The SPE cartridges were dried and subsequently eluted two times with 4 mL methanol into 15 mL PP-tubes (Corning™). All eluted samples were evaporated under a gentle stream of nitrogen gas until reaching a volume of 0.5 mL and 0.5 mL of Milli-Q water was added before analysis.
11 2.3.2 Toxicological analysis
Extraction of water samples (0.9-2.5 L, depending on sample type) was conducted using an automatic SPE system (SPE-DEX, Horizon Technology, Salem, NH, USA) with HLB extraction disks (Atlantic HLB-H Disks, diameter 47 mm; Horizon Technology, Salem, NH, USA). The disks were conditioned with 280 mL methanol and 420 mL Millipore water, and then the water samples were loaded to the disks at a flow rate of 50 mL/min. After washing twice with 24 mL 5% methanol in Millipore water, the disks were dried under vacuum for 30 min, and eluted 3 times with 25 mL methanol. The samples were then evaporated under a gentle nitrogen stream at 35°C. Final extracts were in 0.5 mL ethanol. As an operational control blank sample, the same volume of Milli-Q water was concentrated by solid-phase extraction, as described above.
The enrichment and dilution of the samples were expressed as relative enrichment factor (REF), calculated as described by Escher et al. [12]. The initial water value was 0.9-2.5 L per sample which was extracted to a volume of 0.5 mL, leading to an enrichment factor of 1800-5000. When incubated with the cells, the concentrated water samples were diluted 100-fold with the cell medium to get a final concentration of 1% ethanol and a REF value of maximum 18-50, depending on sample type. The concentrated water samples were then analyzed in a dilution series with lower REF values. REF >1 means that the water sample is concentrated, while REF <1 means that the water sample is diluted compared with the initial water.
2.4 Instrumental analysis of OMPs and quality control
The water and sludge samples were analyzed by a DIONEX UltiMate 3000 ultra-high pressure liquid chromatography (UPLC) system (Thermo Scientific, Waltham, MA, USA) coupled to a triple quadrupole mass spectrometer (MS/MS) (TSQ QUANTIVA, Thermo Scientific, Waltham, MA, USA).
A Kinetex® Biphenyl column (100 mm × 2.1 mm i.d, 2.6 µm particle size, Phenomenex), preceded by an Accucore aQ guard column (10 mm × 2.1 mm i.d, 3 mm particles, Thermo Fisher Scientific, San Jose, CA, USA) from the same manufacturer, was used for chromatographic separation of target OMPs in water samples. In addition, a Kinetex® Biphenyl column (100 mm × 2.1 mm i.d, 2.6 µm particle size, Phenomenex) was used for separation of target OMPs in sludge samples.
12 Furthermore, an acquity BEH C18 column (50 mm x 2.1 mm, 1.7 µm, Waters Corporation, Manchester, UK) was used for chromatographic separation of hormones.
Heated electrospray ionization (H-ESI) was used to ionize the target compounds. The spray voltage was set to static: positive ion (V) 3500. Nitrogen (purity >99.999%) was used as sheath gas (50 arbitrary units), auxiliary gas (15 arbitrary units), and sweep gas (2 arbitrary units). The vaporizer was heated to 400°C and the capillary to 325°C.
The mobile phase consisted of MilliQ water with 0.1% formic acid and methanol with 0.1% formic acid. The flow rate was 0.6 mL/min and run time was 16 min, with switched positive and negative electrospray ionization modes. The mobile phase for hormone analysis consisted of MilliQ water with 5 mM ammonium acetate and acetonitrile and the flow rate was 0.5 mL/min and run time was 15 min using switching positive and negative electrospray ionization modes.
The chromatography data acquisition was performed in positive and negative mode using selected- reaction monitoring. Xcalibur software (Thermo Fisher Scientific, San Jose, CA, USA) was used for optimizing the instrument methods and running samples. The data obtained were evaluated using TraceFinderTM 3.3. software (Thermo Fisher).
The performance of the method was assessed with regard to its linearity, limit of quantification (LOQ), relative recovery, precision, and blanks. The linearity of the calibration curve was tested in the range 0.01 ng/L to 10 000 ng/L for water samples, 0.01 to 1000 ng/mL for sludge samples and 0.01 to 250 ng/mL for hormone analysis. The calibration curve was measured twice, at the beginning and end of the sequence, to check instrument stability. The calibration was prepared in tap water for water samples and acetonitrile/water (1/1) for sludge samples. LOQ was calculated as half the lowest calibration point in the calibration curve where the relative standard deviation of the average response factor was <30% (in some cases, one or two points at low concentration levels had to be removed). The peak area corresponding to this concentration was used to calculate LOQ for each individual compound in each sample (Table SM2 in SM). The precision of the method was evaluated by the repeatability of the study. For this purpose, duplicates were run for every 10th sample.
The physical-chemical properties of all target compounds detected (n=178) are reported in Table SM2.
13 2.5 Fish embryo toxicity tests
During recent years, the zebrafish embryo toxicity test (ZFET) has been increasingly used as an alternative to acute fish toxicity tests and is considered a useful intermediate between cell culture and in vivo tests. The ZFET is an effect-based generic toxicity test using whole-organism zebrafish embryos with active metabolism. The ZFET is derived from the standardized OECD Test Guideline 236 [OECD 2013], but prolonged in exposure time, and includes measurements of lethal and sub-lethal responses in developing zebrafish embryos from fertilization up to 6 days of age [13].
2.5.1 Sample description
In the present study, water samples from four WWTPs were evaluated for zebrafish embryo toxicity according to current standards. Screening for toxicity was also performed for seven more WWTPs, using minor treatment groups. Extracts of the water samples were prepared at the Department of Water and Environment, SLU, to a final concentration of 2000 times the water concentration, and dissolved in dimethyl sulfoxide (DMSO). The extracted sewage samples were then kept frozen at -20°C until the day of exposure. In order to avoid toxicity from the solvent DMSO, the extracted samples were diluted 1000 times, resulting in 0.1% solvent concentration in the exposure solution. Each extract was diluted by mixing 20 µL of the extracted sample with 20,000 µL of carbon-filtered tap water in Petri dishes. Thus, the extracted samples were diluted to an exposure concentration of twice the original water concentration, and then used for testing toxicity in zebrafish embryos.
2.5.2 Zebrafish husbandry and egg collection
Adult stock zebrafish were maintained in a flow-through system of carbon-filtered tap water (pH
= 7.9; hardness = 6.7 d; conductivity = 468 µS/cm). The temperature was kept at 26°C and the photoperiod was 12:12 h light:darkness. The fish were fed daily with commercial flakes (SERA Vipan) as the staple feed. Adult fish were placed in stainless steel mesh cages in aquariums on the
14 afternoon of the day before egg collection. Four 10-L aquariums were used, with 10-14 fish in each. Fish were allowed to spawn for 30 min on the following morning, from onset of light. Eggs collected from different aquariums were pooled, rinsed free from debris, and transferred into clean water in Petri dishes.
2.5.3 Zebrafish embryo toxicity tests
Groups of eggs were transferred to the test solution. Fertilized eggs in at least the four-cell stage were selected under stereo microscope and transferred individually to wells in 96-well polystyrene plates (Corning Inc., USA), together with 250 µL of the exposure solution. For each exposure group, the distribution of eggs was arranged according to a randomized block design within the well plates, to avoid edge interference affecting measured endpoints. For the duration of the exposure, the well plates were covered with Parafilm to eliminate contamination and evaporation, and then kept at 26 ± 1 °C. There was no exchange of test solution over time, so the embryos were exposed statically from fertilization to 6 days of age. Control groups, kept in carbon tap water with 0.1% DMSO solvent, were included for each test. The embryos were observed at 24, 48, and 144 hours post-fertilization (hpf) for specific endpoints in three categories; lethal categorical, sublethal categorical, and sublethal continuous (Table 1). Observation of embryos was done using stereo and inverted microscopes (Leica EZ4D and Olympus CKX41). The hatching time for each embryo was recorded by a time-lapse camera (Canon EOS 500D) that photographed the plates automatically every hour between 48 and 144 hpf. The hatching times for each individual embryo were determined by visual examination of the time-lapse photos.
At 144 hpf, embryo behavior was evaluated using an automated computerized video recording system (ViewPoint Zebrabox®, ViewPoint, France). The swimming activity of each embryo in the 96 well plates was automatically recorded in alternating dark and light phases during 40 min.
The assessment of the swimming activity began with 10 min of acclimation in light, followed by three alternating 5-min light and dark intervals. The swimming activity of each individual was recorded continuously, and the output was summarized in 10-s intervals. The data on swimming activity were evaluated in terms of three variables: swimming activity (mm/min), total swimming distance (mm/30 min) during dark, and total swimming distance during light.
15 2.5.4 Statistics
All data were analyzed using R Studio Version 1.1.463 – © 2009-2018 RStudio, Inc. One-way ANOVAs with Dunnet’s post hoc test were used to analyze the continuous ZFET endpoints. The binary data were analyzed using the Fisher exact test. One-way ANOVAs with pairwise comparisons were used to analyze the behavioral endpoints.
Table 1. Description of the different endpoints at 24, 48, and 144 hours post-fertilization (hpf) in zebrafish embryo toxicity tests
Endpoints Description 24 48 144 (hpf)
Lethal categorical
Coagulation Embryo is coagulated with no structures X X X
Lack of heartbeat Embryo has no visible heart beat X X
Sublethal categorical
Tail deformation Tail is shorter than normal, or curved X X X
Eye deformation Eye is not normally round with a visible lens X X X
Head deformation Head is not normally developed X X X
Reduced pigmentation Embryo shows reduced pigmentation X
Edema Edema is present X X
Tremor Embryo shows tremor X X
Side-laying Embryo lies on the side X
Unhatched Embryo is alive, but has not hatched at the observation
time X
Sublethal continuous
Movements Number of movements are counted during 30 seconds and then re-calculated to movements per minute X
Heart rate Time for 30 heartbeats is recorded and converted to
beats per minute X
Time to hatching Time to the first 1-h interval time-lapse photo where
the embryo is hatched 48-144 hpf
Swimming activity Swimming distance in alternating light and dark X
Modified from Carlsson et al. [13].
16 2.6 Effect-based in vitro bioassays
The concentrated water samples were analyzed in quadruplicate in effect-based in vitro bioassays, representing important toxicity endpoints. The 11 bioassays used are summarized in Table 2. For each assay, a standard curve for a known positive control for that specific assay was analyzed.
Table 2. Summary of bioanalytical methods used in effect-based in vitro bioassays
Endpoint
Assay Additional
treatment
Positive control Bioanalytical equivalent concentration (BEQ) presented
Reference
Cell viability MTS test in AREc32 cells
- 10% DMSO (LOEC)
Cell viability CellTiter-Glo assay in VM7Luc4E2
- 10% DMSO (LOEC)
Cell viability MTS test in AR- EcoScreen cells
- 10% DMSO (LOEC)
Androgen receptor activation
AR-EcoScreen - DHT DHT equivalents [14]
Androgen receptor antagonism
AR-EcoScreen DTH Hydroxy-flutamide Flutamide
equivalents
[14]
Estrogen receptor activation
VM7Luc4E2 - Estradiol Estradiol
equivalents
[15]
Estrogen receptor antagonism
VM7Luc4E2 Estradiol Raloxifen Raloxifen
equivalents
[15]
Oxidative stress response (Nrf2 activity)
AREc32 - tBHQ tBHQ equivalents [16]
Aryl hydrocarbon receptor activation
Transiently transfected HepG2 cells
- TCDD TCDD equivalents [17]
NFĸB activation Stably transfected HepG2 cells
- TNFα TNFα equivalens [17]
Glucocorticoid receptor activation
GR-GeneBLAzer - Dexamethasone Dexamethasone
equivalents DMSO - dimethyl sulfoxide, LOEC - lowest observed effect concentration
Bioactivities of water samples and positive controls were normalized to plate vehicle controls, set to 1. For nuclear receptor-based assays, standard curves for positive controls were obtained by fitting data to a four-parameter sigmoidal curve fit using GraphPad Prism 7. For Nrf2 and NFĸB, the standard curves for positive controls were based on linear regression using GraphPad Prism 7.
The bioanalytical equivalent concentration (BEQ) for REF 1 for each sample showing an activity was calculated using the dose-response relationship for the positive control and corrected for the
17 dilution factor. Removal efficiency was calculated by comparing the BEQ value at REF 1 for WWTP influent and effluent samples. For each bioassay, the limit of detection (LOD) was calculated as 1 plus 3 times the standard deviation (SD) of the normalized vehicle control values.
For antagonistic assays, the LOD was calculated as 1 minus 3 times the SD. A sample was classified as “active” if the observed activity was above (for agonistic activities) or below (for antagonistic activities) the LOD for that specific assay.
18
3. Results and discussion
3.1 Occurrence of OMPs in wastewater influent and effluent, and in recipient waters
Of the 225 target OMPs analyzed in wastewater (influent and effluent) and recipient water (upstream and downstream), including pharmaceuticals, personal care products, industrial chemicals, PFASs, hormones and pesticides, 158 contaminants were detected in at least one sample (Table SM4, Tables 3 and 4). It should be noted that the occurrence of OMPs in influent wastewater mainly reflects consumption or usage of the compounds at a specific location, while the effluent concentration shows treatment efficiency of the respective WWTP.
Mean concentrations detected for the compounds ranged from ng/L to mg/L in wastewater samples and from ng/L to µg/L in surface water samples. Given this wide range of concentrations, it is noteworthy that a group of only 20 OMPs was responsible for 70% of the combined concentration of pollutants in WWTP influent and effluent and surface water (Tables 3 and 4). These compounds presented not only in high concentrations, but also at a high frequency of detection (>50%) (Figure 1). The group included two industrial chemicals (tetraethyleneglycol, laureth-5 and di-(2- ethylhexyl)phosphoric acid), 15 pharmaceuticals (salicylic acid, diclofenac, losartan, valsartan, venlafaxine, oxazepam, lamotrigine, carbamazepine, tramadol, hydrochlorothiazide, theophyline, furosemide, ranitidine, bicalutamide, and metformin), and the stimulants caffeine and nicotine (Figures 1-5, Table SM4, Tables 3 and 4). The concentrations were within the range reported previously for antidepressants and antiepileptic drugs in Swedish WWTPs and surface water [1, 4, 18]. Fick et al. [18] performed a screening study in Sweden for 101 pharmaceuticals in wastewater, surface water, sludge, and biota samples, and reported similar concentration levels to those found for most pharmaceuticals analyzed in the present study.
19 Table 3. Detection frequency (DF, %) and minimum, maximum, and median concentrations (ng/L) of organic micropollutants (OMPs) in wastewater treatment plant (WWTP) influent, effluent, and sludge samples
Compound Influent, ng/L Effluent, ng/L Sludge, ng/f (dry weight)
DF Min Max Median DF Min Max Median DF Min Max Median
2,2'-Dimorpholinyldiethyl-ether 100% 3 33 16 97% 5.1 150 57 0%
N-Butyldiethanolamine 59% 9.5 480 34 59% 5 370 21 0%
Tetraethylene glycol 100% 8900 31000 22000 100% 190 5000 630 0%
Dibutyl phosphate 100% 28 4100 68 100% 16 3900 45 0%
Mono-n-butylphosphoric acid 85% 18 310 57 83% 9.9 1900 38 100% 13 190 69
Sulfaclozine 7% 4.6 9.2 6.9 76% 4.7 14 7.6 0%
Laureth-5 100% 940 60000 15000 90% 92 1600 170 0%
Tributyl citrate acetate 96% 8.1 250 72.5 100% 8.1 540 41 100% 14 750 90
N,N-Dimethyltetradecylamine 74% 79 12000 1950 7% 54 310 182 0%
Di-(2-ethylhexyl)phosphoric acid 100% 15 7100 350 100% 4.5 1900 100 0%
Thiabendazole 100% 2.1 11 4.5 100% 3.9 18 6.6 0%
Propylparaben 81% 0.1 1.55 0.515 38% 0.11 0.25 0.15 0%
Ethylparaben 44% 0.08 2.9 0.625 7% 0.11 0.11 0.11 50% 3.05 8.9 3.9
Methylparaben 7% 0.27 0.85 0.56 0% 96% 17 230 61
Pyridoxine (Vitamin B6) 96% 180 450 275 90% 37 130 68 100% 46 360 142
Nicotinamide 37% 117 1200 240 38% 100 220 160 100% 38 650 199
Nicotine 100% 4800 10000 6600 97% 21 610 63 100% 18 770 68
Caffeine 100% 35000 64000 53000 100% 25 7500 370 100% 8.3 590 46
Paraxanthine 56% 49 195 100 31% 47 110 65 0%
Fenbendazole 78% 1.1 12 1.9 17% 1.1 2.1 1.3 100% 5 280 48
Levamisole 33% 1.5 8.7 2.6 66% 1.5 31 3.2 54% 0.43 14 1.5
Mebendazole 78% 1.8 15 4.3 83% 1.8 12 3.4 71% 5 900 18
Sulisobenzone 100% 1100 3000 2000 100% 640 3000 1600 92% 15 1100 37.5
Benzophenone 100% 47 540 90 100% 36 400 70 0%
Oxybenzone 93% 13 101 35 76% 7.6 39 15 100% 2.9 230 12.5
3-(4-Methylbenzylidene)camphor 100% 4.5 29 10 100% 14 56 33 100% 42 630 283
Metronidazole-OH 19% 5.5 180 58 97% 12 210 86 0%
Metronidazole 30% 3.4 35 7.3 90% 2.3 56 17 0%
Trimethoprim 100% 2.8 101 36 100% 9.1 160 70 42% 0.43 39 3.425
Erythromycin 100% 2 120 17 97% 8.9 140 40 92% 0.36 12 1.1
Ofloxacin 78% 1.5 45 4.3 69% 1.4 13 3.4 0%
Amoxicillin 100% 26 210 130 100% 39 260 140 0%
Ciprofloxacin 100% 4.9 560 28 100% 1.3 150 24 0%
Tetracycline 96% 34 445 137 52% 20 120 39 0%
Chloramphenicol 37% 6.3 14 8.0 31% 6.9 12 8 0%
Clindamycin 96% 4.9 78 12 100% 14 170 68 96% 0.91 21 9.1
Sparfloxacin 100% 14 170 49 97% 11 1500 505 0%
Clarithromycin 93% 1.1 55 15 83% 3.5 88 23 88% 1.3 38 5.6
Roxithromycin 26% 1.8 17 4.5 31% 1.1 27 3.6 0%
Azithromycin 96% 5 110 17 93% 1.7 92 49 92% 10 210 22
Sulfamethoxazole 100% 2.1 160 54 100% 6.9 150 47 0%
Atenolol 100% 285 1800 860 100% 32 1300 750 92% 1.1 47 4.95
Albuterol (Salbutamol) 100% 2.7 12 7.6 97% 4 16 9.3 0%
Sotalol 100% 4.7 290 115 100% 1.9 340 140 83% 0.9 730 31
Metoprolol 100% 480 1100 805 100% 500 1800 1100 100% 41 800 165
Propranolol 100% 10 84 41 100% 15 110 64 100% 50 510 128
Terbutaline 96% 1.8 9.7 4.9 55% 1.8 6.2 3.1 0%
Carazolol 0% 0% 0%
Iopromide 15% 190 1000 430 17% 15 1035 330 0%
Amidotrizoic acid 78% 25 9200 500 83% 50 6400 925 0%
Lidocaine 100% 110 1483 230 100% 180 1400 350 100% 3.2 570 18
Tramadol 100% 380 1200 650 100% 430 1600 1100 100% 19 580 160
20
Codeine 100% 110 790 335 100% 61 680 190 96% 1.8 110 18
Oxycodone 100% 11 36 22 100% 6.9 69 24 0%
Venlafaxine 100% 220 880 450 100% 190 1100 620 100% 20 780 120
Fluoxetine 100% 1.3 13.5 5.3 97% 2 26 6.8 100% 19 310 120
Mirtazapine 100% 57 190 120 100% 7.4 260 130 100% 90 2235 220
Citalopram 100% 17 260 145 100% 34 370 220 100% 93 780 340
Paroxetine 52% 1.4 41 2 31% 1.6 46 2.6 100% 7.6 1500 45.5
Amitriptyline 100% 6.4 61 24 100% 4 83 34 100% 84 16000 250
Sertraline 100% 8 40 17 100% 0.91 62 23 100% 170 920 560
Oxazepam 100% 120 370 235 100% 150 460 320 100% 11 250 42
Diazepam 4% 33 33 33 7% 8.3 70 39 0%
Norsertraline 7% 75 76 76 28% 71 110 83 100% 190 2000 565
O-Desmethylvenlafaxine 100% 445 1500 840 100% 430 1600 810 0%
N-Desmethylcitalopram 100% 6.2 77 46 100% 21 110 74 100% 27 870 270
Norfluoxetine 22% 0.09 0.15 0.11 14% 0.11 0.14 0.13 100% 6.5 270 80
Lamotrigine 100% 210 860 360 100% 490 2000 928 100% 55 300 120
Primidone 93% 1.3 53 14 97% 4.1 120 34 0%
Carbamazepine 100% 74 340 160 100% 150 760 270 100% 35 14000 130
10,11-Dihydro-10-
hydroxycarbamazepine 100% 7 430 160 100% 34 580 160 88% 6.3 790 11
Carbamazepine 10, 11-epoxyde 100% 108 310 170 100% 110 730 280 100% 9.8 2200 52
Oxcarbazepine 44% 11 38 22 38% 8.7 33 17 0%
10,11-dihydrocarbamazepine 0% 0% 0%
Hydrochlorothiazide (HCTZ) 100% 590 2900 1200 100% 76 3700 1500 79% 9.9 230 20
Furosemide 100% 480 2100 1200 100% 8.3 2600 830 100% 25 340 82
Salicylic acid 100% 26 27000 12266 100% 21 1100 52 0%
Phenazone 85% 5.9 37 11 97% 7.2 46 16 0%
Mefenamic acid 30% 1.2 4.4 1.7 79% 1.4 5.8 3.1 0%
Diclofenac 100% 25 623 280 100% 14 1500 800 100% 46 330 125
Telmisartan 100% 3.6 220 11 100% 11 200 30 100% 64 1700 155
Bisoprolol 100% 17 160 75 100% 12 270 88 88% 6.8 350.5 18
Enalapril-maleate 100% 97 340 220 66% 6.9 280 53 0%
Verapamil 89% 0.91 8.7 1.7 90% 0.9 9.4 3.35 83% 5.2 780 21.5
Ramipril 100% 1.4 15 5.3 100% 1.6 15 5.7 0%
Amlodipine besylate 100% 6.6 46 17 97% 1 31 7.4 0%
Diltiazem 100% 1.7 44 9.8 97% 1.2 38 9.85 33% 1.2 11 4.4
Losartan 100% 460 2200 1200 100% 100 2800 1183 100% 58 540 240
Irbesartan 100% 5.3 370 57 100% 26 600 98 100% 1.9 130 39.5
Valsartan 100% 110 2100 244 100% 11 1600 360 100% 11 88 30
Metformin 100% 8950 51000 22000 100% 110 57000 5800 100% 92 6400 255
Glybenclamide 0% 21% 2.1 3.5 2.7 38% 5.9 160 29
Fluconazole 100% 6.2 180 78 100% 13 250 99 100% 0.81 7.3 2.75
Climbazole 100% 7.2 65 20 100% 7.2 42 19 100% 5.9 230 41
Ifosfamide 11% 2 14 12 10% 4.1 28 8.4 0%
Methotrexate 81% 0.71 8.1 2.9 86% 0.94 10 3.9 79% 8.1 92 19
Fenofibrate 0% 0% 0%
Simvastatin 100% 23 290 100 93% 21 130 49 100% 21 700 280
Bezafibrate 81% 9.4 190 46 79% 5.7 514 27 71% 7.5 125 51
Atovastatin (Lipitor) 100% 70 560 240 93% 1.4 460 52 100% 6.3 120 28
Gemfibrozil 93% 7.4 319 29 90% 8.3 576.8 44 88% 14 170 32
Fexofenadine 100% 98 790 300 100% 130 1000 480 100% 54 840 230
Loratadine 30% 0.9 1.7 0.94 79% 0.9 2.9 1.4 100% 13 401 41.5
Cetirizine 100% 87 270 140 100% 110 450 220 100% 24 500 148
Panthenol 100% 7300 19000 15000 45% 88 2600 470 0%
Theophylline 100% 1400 2800 2050 100% 3.8 610 58 96% 4.3 1400 11
Chloroquine 41% 0.7 5.5 1.4 31% 0.87 15 1.35 100% 0.8 230 9.7
Genistein 100% 3.5 43 11 93% 0.97 11 3.3 96% 5 450 40
Memantine 100% 8.2 37 15 100% 15 66 35 100% 2.8 21 7.55
Clozapine 93% 7 70 19 93% 6.2 59 17 100% 43 510 192
Chlorzoxazone 100% 4 25 9.6 48% 1.9 25 4.55 0%
