This report presents the outcome of the Nordic Council of Ministers’ founded project “Pharmaceuticals in Northern environments; what, where and how much?”. This Nordic project has been led by Pernilla Carlsson in close collaboration with Kristín Ólafsdottir and Arndís Sue-Ching Löve (Háskóli Íslands), Johan Lindberg (RISE, Sweden), Susanne Vävare (Ålands landskapsregering), Christian Vogelsang, Mona Eftekhar Dadkhah and Malcolm Reid (NIVA). Everyone has contributed with data and statistics from their respective country on pharmaceutical sales statistics and waste water treatment plant (WWTP) processes. Literature review, reporting, calculations of human excretion and WWTP removal efficiency have mainly been conducted by Christian Vogelsang, Mona Eftekhar Dadkhah, Kristín Ólafsdottir, Arndís Sue-Ching Löve, Malcolm Reid and Pernilla Carlsson. We thank everyone involved for a very good cooperation.
Tromsø, November 2019
Pernilla Carlsson, Ph.D., Project manager
Environmental and analytical chemistry, NIVA
The present study has investigated and collected sales statistics of pharmaceuticals in Sweden, Norway and Iceland. A literature review of waste water treatment plants (WWTP) and the efficiency of different WWTP techniques was conducted and combined with a literature review of metabolization of pharmaceuticals in humans. These data have been linked and used for calculations of the amount of pharmaceuticals and their metabolites that actually reaches the aquatic recipient. The most common used pharmaceuticals differ slightly between the Nordic countries investigated, although many of them are common in all of the countries.
Since pharmaceuticals often are metabolised to a large extent within the body and thereafter excreted as metabolites into the WWTPs, an efficient removal of the little fraction of parental compounds still present does not take into account any metabolites released into the aquatic environment. A future recommendation is to include metabolites of pharmaceuticals in environmental impact studies to be able to assess and account for the chemicals that actually pass through a WWTP to the aquatic environment.
Denne studien har samlet og undersøkt salgsstatistikk for legemidler i Sverige, Norge og Island. Litteraturundersøkelser av effektiviteten ved forskjellige typer renseverk og metabolisering av legemidler i mennesker er utført og kombinert for å kunne estimere hvor mye legemidler og metabolitter som går ut årlig fra renseverk i Norden og inn i det akvatiske miljøet.
Det er ikke avdekket noen store, vesentlige forskjeller i forbruk av legemidler i de undersøkte landene, selv om det varierer hvilke legemidler som er de aller mest brukte.
Mesteparten av legemidlene metaboliseres ofte i kroppen og det er derfor mer metabolitter enn hovedstoff som oftest havner i renseverkene. En høy renseeffektivitet av hovedstoffet er dermed ikke enslydende med at mye av den totale massen hovedstoff og metabolitter faktisk rensens bort før det når det miljøet. Våres anbefaling er derfor at studier i fremtiden bør se mer på utslipp av metabolitter til det akvatiske miljøet, noe som er ekstra viktig mhp. eventuelle miljøeffekter av metabolitter.
Tittel: Legemidler i Norden; hva, hvor og hvor mye?
Forfattere: Pernilla Carlsson, Kristín Ólafsdottir (Háskóli Íslands), Arndís Sue-Ching Löve (Háskóli Íslands), Christian Vogelsang, Mona Eftekhar Dadkhah, Johan Lindberg (RISE), Malcolm Reid
Utgiver: Nordisk Ministerråd
Wastewater treatment plants (WWTPs) are designed to meet their discharge permits focusing on the removal of suspended solids (SS) (i.e. particulate matter) and nutrients such as biodegradable organic matter, phosphorous or nitrogen depending on the local treatment requirements. The discharge permits are stricter in regions that are considered as sensitive to eutrophication than in so-called normal areas, which is the case for the Baltic Sea, Kattegat and Skagerrak and their associated freshwater catchment areas. As stated in the EU Directive 91/271/EEC, all discharges from agglomerations >2,000 person equivalents (PE) to normal and sensitive areas need to undergo secondary treatment, which implies a minimum of 70% removal of the biological oxygen demand (BOD5) and 75% removal of the chemical oxygen demand (CODCr)Or discharge of a maximum of 25 mg O2/L as BOD5 and 125 mg O2/L as CODCr. There is also an optional limit of 70% removal of SS (<60 mg SS/L in effluent) for 2,000-10,000 PE and 90% removal of SS (<35 mg SS/L in effluent) for >10,000 PE. . Discharges from agglomerations >10,000 PE to sensitive areas also need to comply with a minimum removal of total phosphorous of 80% (90% in the Norwegian regulations) and/or a minimum removal of 70% removal of total nitrogenOr discharge of a maximum of 2 mg P/L (1 mg P/L for agglomerations >100,000 PE) and 15 mg N/L (10 mg N/L for agglomerations >100,000 PE).. These requirements imply relatively advanced biological and/or chemical treatment. In Norway, chemical treatment has been the preferred treatment method (Figure 1), focusing on the removal of phosphorus rather than BOD5. Hence, if phosphorous removal by chemical treatment is already applied (complying with a minimum of 90% removal of total P), secondary treatment is only required when a major upgrading to the WWTP is planned. Furthermore, nitrogen removal is only required in four distinct areas in Norway; Oslo, Lillehammer, Jessheim and Nordre Follo. In Sweden and Åland, the main treatment of waste water is tertiary treatment.
The permits regulating discharges to other areas than those mentioned above are in general less strict. Discharges from agglomerations of <10,000 PE to less sensitive areas (i.e. the Atlantic coast of Norway and Iceland) only need to apply simple sieving (≤1 mm pore size) or septic tank and complying with a minimum of 20% reduction in SS. For discharges from agglomerations of >10,000 PE to less sensitive areas secondary treatment is the standard. Moreover, if the discharge can be documented to not adversely affect the environment in terms of eutrophication or oxygen depletion due to a good water exchange, only primary treatment may be required, which implies a minimum of 20% removal of BOD5 and 50% removal of SS.
Figure 1. Overview of applied treatment principles and capacities in Norway (Berge and Sæther, 2018).
Recent studies have shown that the Baltic Sea receives about 1,800 tonnes of pharmaceuticals each year and the cleaning efficiency of parent compounds are often low (Vieno et al., 2017). On top of this, information regarding the environmental fate of the metabolites of these pharmaceuticals are very scarce. Some metabolites may deconjugate back to the parental compound (Celiz et al., 2009; Ternes et al., 1999) while others might very likely have a biological impact also as metabolites. Pharmaceuticals are, by obvious reasons, designed to have an impact in a biological system. However, little is known about any potential side effects apart from e.g. sex hormones and their interaction with fishes (Perrault 2003). As with all environmental pollutants, it is important to keep in mind that volume and toxicity are not necessarily correlated; certain compounds might be used in small quantities, but still have a high toxicity.
The project aims to investigate and quantify what, where and how much pharmaceuticals are released to the surrounding water bodies through waste water treatment plants in Iceland, Sweden, Åland and Norway. We will focus on the mostly used pharmaceuticals in each country and literature data on WWTP efficiencies on the respective compounds.
All pharmaceuticals are classified according to the Anatomical Therapeutic Chemical (ATC) system according to their anatomical main group, therapeutic and pharmacological subgroup and chemical substance. Compounds listed with several modes of action (i.e. same chemical compound, different ATC codes) were combined in this report. Usage of pharmaceuticals in the Nordic countries were provided as Defined Daily Doses (DDD). A DDD is defined as “the assumed average maintenance dose per day for a drug used on its main indication in adults”. These numbers were recalculated into kilogram/year based on information from WHO (2019). More information regarding ATC system and DDDs can be found in e.g. Sakshaug et al. (2018) and from WHO (2019).
Information on 40 most sold prescription pharmaceuticals in Iceland 2017 and 2018 was provided by the Medicines Registry from the Directorate of Health. This list provided a total number of nationwide defined daily doses (DDD) of sold prescription drugs. The DDD of each drug was converted to kg sold per year by using the doses defined by the WHO Collaborating Centre for Drug Statistics Methodology (WHO) 2019. The number of sold units of pharmaceuticals sold over the counter (OTC) in 2018 were provided by the Icelandic Medicines Agency. The units sold were converted into kg per year. The OTC data was combined with data on prescription drugs. Pharmaceuticals used by hospitals are not included in these numbers and the medication brought into the country by more than 2.3 million tourists per year is unknown (Ferdamalastofa, 2019). Iceland had 356,991 inhabitants as of 1 January 2019 (Statistics Iceland, 2019).
The Norwegian Institute of Public Health (FHI) was contacted and kindly provided statistics for annual usage in Norway of the active compounds. The data includes pharmaceuticals sold over the counter as well as prescriptions. Norway had 5,328,212 inhabitants as of 1 January 2019 (SSB, 2019).
The Swedish Medical Products Agency (SMPA) was contacted and kindly provided statistics for annual usage in Sweden of the active compounds. The data includes pharmaceuticals sold over the counter as well as prescriptions. Sweden had 10,230,185 inhabitants as of 31 December 2018 (SCB, 2019).
Only data regarding usage of antibiotics were available for Åland. The waste water treatment in Åland is tertiary for the main population (ca 29,800 habitants in 2018), and water corresponding to 31,000 person equivalents (PE) are treated each year (personal communication with Lotsbroverket, and Susanne Vävare (Ålands landskapsregering)). This number accounts for industrial waste water as well. Some habitants are not connected to the main WWTP. Due to limited resources in the project, we choose not to do a specific calculation for Åland based on e.g. Swedish sales statistics. However, seen in a holistic, Scandinavian perspective, these 29,800 people would anyway be within the uncertainty of e.g. Åland’s neighbour Sweden and hence, an additional calculation of Åland with sales statistics from its neighbour was considered to not be highly relevant to this report taken the general uncertainty into account.
Information regarding metabolism and excretion of all investigated compounds were collected through a literature study (Table S1). Since details regarding which metabolites are formed and to which extent are often scarce, we have not done any calculations on metabolites released at this time since such numbers would involve very high uncertainty. We realize however, that the metabolites might be responsible for high effects in the environment just as, or even more than the parent compounds. These effects will need to be explored in a later study.
Wastewater treatment principles ranges from no treatment (typically small, private sewages) to advanced biological and chemical methods. Mechanical cleaning (e.g. filtering of large particles) are the most common method in Iceland (68% of the population) and is also very common in large areas of Norway, although chemical and/or biological treatments are applied in Skagerrak and lake areas. Most treatment in Sweden includes biological/chemical steps. All waste water at Åland is treated with biological and chemical treatment. Each type of chemical/biological treatment applied will have different efficiency for individual pharmaceuticals due to different technological settings and their impact on individual pharmaceuticals. It was not possible to take all technological settings into account, in the present report, and there are large data gaps in cleaning efficiencies as well. Therefore, we applied a general approach where primary treatment means only mechanical treatment, and where secondary and tertiary treatments include chemical/ biological treatment with an average efficiency based on literature data for each compound. Iceland only have chemical/biological treatment for about 2% of its population, from two small land-locked towns, Egilstaðir and Hveragerði. Due to other data limitation, we have not taken those 2% into account and all treatment in Iceland has been set to mechanical/no treatment. The calculations for Norway have taken secondary and tertiary treatment efficiencies into account since a much larger percentage of waste water is treated compared to Iceland. Similar calculations were done for Sweden. We applied the Norwegian statistics regarding untreated water to take into account small, rural villages where waste water may not be treated before reaching a recipient in Sweden.
Compounds where data on WWTP efficiency exists were divided into three classes; low (<10% removal efficiency), medium (50%) and high (90%). The amount of parental compound entering the WWTPs were based on human excretion data (Table S1) and the cleaning efficiency (low/medium/high) percentages for these compounds were then applied using the calculations to estimate the release of parent compounds from the WWTPs into the recipient, depending on treatment. Since the treatment efficiency will depend on different techniques, biological/chemical/physical conditions, we applied a low-medium-high percentage instead of an exact value, since it will be too dependent on other factors.
NIVA has access to discharge details from all domestic WWTPs in Norway that have reported to the municipality-government reporting system (KOSTRA). Reported treated wastewater volumes in 2017 from 310 WWTPs with a treatment capacity >2,000 PE were used in the calculations of the yearly discharge estimates. With a total treatment capacity of 6.67 million PE and ca. 648 million m3 of treated wastewater, these WWTPs represented 90% of the total treatment capacity and ca. 98% of all treated wastewater reported in 2017 from all WWTPs ≥50 PE in Norway. All reported sewer overflows from the same WWTPs were also included, amounting to ca. 17 million m3 wastewater. Volume-wise, the WWTPs that applied combined chemical and biological treatment dominated with 42% (Table 1) of the different parent pharmaceutical compounds and sewer overflows. The discharge estimates from Norwegian WWTPs are based on 2017 data.
Table 1. Summary of number of WWTPs applying different wastewater treatment principles and associated volumes treated.
|Treatment||Iceland||Norway (>2000 PE)||Sweden||Åland|
|Mechanical||68%||108||84.4 (21%, incl. CEPT)|
|86||277.4 (42%)||Close to 100%||Close to 100%|
Note: *CEPT = Chemically enhanced primary treatment.
Compound-specific removals by the treatment principles typically applied by the WWTPs in the countries participating in the current survey (see Table 1) were predicted based on available scientific literature.
Information on excretion of pharmaceuticals and their major metabolites was obtained from the following articles: Baselt (2017), Brunton et al. (2018), Moffat et al., (2011), Stanczyk et al. (2013), Shoupe and Haseltine (1993) and WHO (2006 and 2018). Treatment efficiencies were collected through a literature review (Appendix; Table S2).
The country-specific annual discharges, MC, were calculated from the estimated fraction of the excreted parent compound that would pass the WWTPs:
MC = Cn * fp,n * (1 – fR,n)
, where MC is the annual discharge of the parent compound to recipients in country C, is the country-specific consumption of compound n, is the fraction of compound n that is excreted as the parent compound, and is the fraction of compound n that is expected to be removed by the WWTPs treatment in country C:
fR,C,n = fmech,n + fchem * Rchem,n + fbiol * R biol,n + fchem-biol * Rchem-biol,n
, where fmech, fchem, fbiol and fchem-biol are the respective fractions of municipal wastewater that are treated by WWTPs where the main treatment principle is mechanical, chemical, biological or chemical-biological, and Rmech,n, Rchem,n, Rbiol,n and Rchem-biol,n are the respective removal ratios of compound n by WWTPs applying the indicated treatment principles.
Iceland was able to provide sales statistics for both 2017 and 2018. For all calculations, the 2018 statistics were used unless something else is stated since 2018 was used for the other countries. However, Iceland 2017 statistics are included in this report in the appendix (Table S3). All sales statistics used for calculations (2018 numbers) are shown in Table 2–4 for Iceland, Norway and Sweden. No sales statistics were available for Åland alone and is therefore not presented here. Diclofenac was listed with different ATC codes for Sweden compared to Iceland and Norway.
Table 2. The 38 most used pharmaceuticals in Iceland 2018.
|ATC index||Active substance||mg/DDD||Millions DDD/|
|N02BE01||Paracetamol + OTC||3000||3||16,205|
|N02AJ06||Codeine and paracetamol||100 + 3,000||2||6,520|
|M01AE01||Ibuprofen OTC||1,200||No info||4,351|
|B01AC06||Acetylsalicylic acid + OTC||160||19||4,304|
|R06AA02||Diphenhydramine OTC||200||No info||1,962|
|C09DA01||Losartan and diuretics||50||5||241|
|C09DA03||Valsartan and diuretics||80||1||114|
|C03EA01||Hydrochlorothiazide and potassium-sparing agents||25||3||76|
|G03AA07||Levonorgestrel and ethinylestradiol||1.5 + 0.025||3||4|
Table 3. The 35 most used pharmaceuticals in Norway 2018.
|ATC-code||Active substance||mg/DDD||Millions DDD/|
Note: * In combinations with Paracetamol. Codeine figures here alone.
Table 4. The 26 most used pharmaceuticals in Sweden 2018.
|ATC-code||Active substance||mg/DDD||Millions DDD/|
|kg sold/ year (2018)|
|A06AG11||Sodium lauryl sulfoacetate***||0.3||261|
*Exact DDD usage is not provided, it is ≥38,000 DDDs.
**Consumer data from 2017.
***mg/DDDs not provided, only information about units sold.
Table 5 shows fraction of pharmaceuticals being excreted as parents and metabolites in Norway. The ratios presented here are presented in more detail in Table S1 as well and used for calculations for all countries in the present study. In general, the majority of releases are metabolites of pharmaceuticals.
Table 5. Estimated annual human excretion of parent compounds and associated metabolites for Norway based on consumption data for 2018.
|CNorway||CNorway * fp||CNorway * fm|
|ATC-code and name||fp||fm||kg/y||kg/y||kg/y|
|N02BE01 - Paracetamol||0.02||0.98||252,850||5,057||247,793|
|A10BA02 - Metformin||1.00||0.00||58,287||58,287||-|
|M01AE01 - Ibuprofen||0.05||0.95||37,658||1,883||35,775|
|B01AC06 - Acetylsalicylic acid||0.01||0.99||19,324||193||19,131|
|J01CE02 - Phenoxymethylpenicillin||0.30||0.70||10,000||3,000||7,000|
|M01AE52 - Naproxen||0.10||0.90||9,500||950||8,550|
|C10AA05 - Atorvastatin||0.02||0.98||3,458||69||3,389|
|N02AX02 - Tramadol||0.29||0.71||2,400||696||1,704|
|A02BC02 - Pantoprazole||0.01||0.99||2,155||22||2,133|
|J01CA08 - Pivmecillinam||0.60||0.4||1,800||1,080||720|
|C10AA01 - Simvastatin||0.01||0.99||1,796||18||1,778|
|N02AJ06 – Codeine*||0.11||0.89||1,500||165||1,335|
|A02BC05 - Esomeprazole||0.01||0.99||1,446||14||1,432|
|C09CA01 - Losartan||0.04||0.96||1,409||56||1,353|
|M01AB05 - Diclofenac||0.01||0.99||1,100||11||1,089|
|R06AE07 - Cetirizine||0.06||0.94||670||39||631|
|C09CA06 - Candesartan||0.26||0.74||512||133||379|
|N05BA04 - Oxazepam||0.01||0.99||450||4.5||446|
|M01AE52 - Esomeprazole||0.01||0.99||380||3.8||376|
|N06AB10 - Escitalopram||0.08||0.92||379||30||349|
|N05CF01 - Zopiclone||0.09||0.91||361||31||330|
|C08CA01 - Amlodipine||0.05||0.95||327||16||310|
|H02AB06 - Prednisolone||0.16||0.84||220||35||185|
|R06AX27 - Desloratadine||0.02||0.98||216||3.7||212|
|R05DA01 - Ethylmorphine||0.04||0.96||150||6.6||143|
|C09AA05 - Ramipril||0.02||0.98||144||2.9||141|
|A01AA01 - Sodium fluoride||0.76||0.24||61||46||15|
|R01AA07 - Xylometazoline||ND||ND||58||-||-|
|R03AC02 - Salbutamol||0.30||0.7||18||5.5||13|
|H03AA01 - Levothyroxine sodium||1.00**||7.4||-||7.4|
|C07AB02 - Metoprolol||0.03||0.97||6.9||0.21||6.7|
|G03AA07 - Levonorgestrel||1.00**||4.5||-||4.5|
|R01AD09 - Mometasone||0.01||0.99||3.6||0.036||3.6|
|G03AA07 - Ethinylestradiol||0.01||0.99||0.90||0.009||0.89|
|G03CA03 - Estradiol||0.06||0.94||0.50||0.32||3.47|
Note: fp and fm are the fractions of distributed parent compound excreted, and annual discharge of its metabolites, respectively.
*Codeine in combinations with paracetamol; codeine figures here alone.
**Levothyroxine sodium and levonorgestrel are assumed to not metabolise to any large extent.
Estimated removal efficiencies of the different pharmaceutical compounds by mechanical, chemical, biological and chemical-biological treatment processes are presented in Table 6. Data from this table are relevant mainly for Sweden, Åland and Norway, since Iceland lack chemical and biological treatments, except for in Egilsstaðir. However, the population in Egilsstaðir is only ~2% of the Icelandic population and are therefore not included in these estimates. Since no sales statistics were available for Åland, no further calculations have been made either. Based on the present data and the knowledge that Åland has tertiary treatment on its main WWTPs, a realistic estimate of released pharmaceuticals and metabolites could be based on sales statistics in Sweden, and the information in Table 6 regarding treatment efficiency for different pharmaceuticals.
Table 7–9 show how much of the most sold compounds in Iceland, Norway and Sweden that are excreted as parent compound and the treatment efficiency/excreted parent compound for the different WWTPs in each country. The influent is the unmetabolised parent compound (for details on metabolisation, see Table S1). The effluent is the amount that, according to Table 6 would be degraded with a chosen treatment. Since most of the parent compounds are metabolised within the human body, a very low percentage of the original compound actually reaches the receiving water bodies. Tramadol (17%) and candesartan (15%) are the only pharmaceuticals on the Norwegian list where >10% of the distributed dose is expected to pass through humans and WWTPs undegraded. It is worth mentioning that these numbers do not take deconjugation of parental compounds into account and that the percentage might be different for other, less used pharmaceuticals. The numbers for Iceland are higher; >10% of a distributed parent compound are released from WWTPs for 10 of the compounds on the list. However, the Icelandic list contains more pharmaceuticals than the Norwegian list and those with high percentage of parent compound passing through the WWTPs are not on the Norwegian compound list.
Table 6. Estimated removal efficiencies of the different pharmaceutical compounds by mechanical, chemical, biological and chemical-biological treatment processes.
|No||ATC code||Compound||Removal efficiency for treatment categories|
Note: Coloured backgrounds (green, orange, red) indicate that there exist data to support the claim.
Green = results from several full scale WWTPs; high removal has been well documented with less advanced treatment, high removal may hence be assumed with more advanced treatment.
Orange = only results from 1–2 full scale plants or pilot scale studies exist.
Red = Predicted from modelling or chemical-physical properties.
* Based on modelling data.
** Formation of parent compound.
***Only pilot plants.
Table 7. Influent Table and effluent of the most used pharmaceuticals in Iceland 2018 as kg/year.
|Mechanical (68%)||Untreated (30%)||Total effluent (kg)||% of distributed parent released from WWTP|
Note: The total amount released in Icelandic waters take percentage of population with certain treatment (given in parentheses) into account. The headlines show how much (percentage) of the national sewage that is treated by which treatment, while the two last columns show the sum of the total effluent and total amount of parent compound released nationwide.
* In combinations with paracetamol. Codeine figures here alone.
** Including potassium-sparing agents.
Table 8. Influent and effluent of the most used pharmaceuticals in Norway 2018 as kg/year.
|Infl.||Effl.||Infl.||Effl.||Infl.||Effl.||Infl.||Effl.||Effl.||Effl.||Ratio of TADPC*|
|Acetylsalicylic acid||40||36||60||6||7.6||0.76||81||8.1||5.0||56||0.29 %|
|Sodium fluoride||9.6||8.6||14||n.e.||1.8||n.e.||19||n.e.||1.19||10||16.1 %|
|Levothyroxine Sodium||0.000||0.0||0.000||n.e.||0.000||n.e.||0.000||n.e.||0.000||0||0.00 %|
* TADPC = total of annually distributed parent compound
Note 1: Total amount discharged from the WWTPs shown are not including the “unknown” discharges, particularly from the chemical treatment plants, due to lack of data and research on this. Note that the given discharges are therefore potentially significantly smaller than the actual discharges. These estimated discharges are marked in red.
Note 2: The influent to each type of treatment plant is the ratio of the total sum of all excreted unmetabolised parent compound (Table 5) that is expected to be received by this type of treatment based on the ratio of the Norwegian population served by this type of treatment plant (percentages shown in heading). The effluent is the estimated residual amount that, according to Table 6, would not be removed applying that type of treatment. The two last columns show, in order, the sum of the parent compound in all effluents from all WWTPs and the percentage that this represents of the total annually distributed parent compound in Norway.
Table 9. Influent and effluent of the most used pharmaceuticals in Sweden 2018 as kg/year.
|Chemical-biological (97%)||Untreated (3%)||Total||% of distributed parent released from WWTP|
|Sodium lauryl sulfoacetate||253||253||8||261||100%|
Note: The influent is the unmetabolised parent compound (for details on metabolisation, see Table S1). The effluent is the amount that, according to Table 6 would be degraded with a chosen treatment (headlines in the table). The total amount released in Swedish environment take percentage of population with certain treatment (given in parentheses) into account. The two last columns show the sum of the total effluent and total amount of parent compound released nationwide.
Since most of the pharmaceuticals investigated in this study are metabolised to a large extent in the human body to metabolites, any percentage of “cleaning efficiency” close to 100% for these compounds does not take metabolites into account. However, no/very little research on the removal efficiency of metabolites (as well as re-formation of parental compounds) was found and could therefore not be included in this study. Table S2 is a literature review of treatment efficiencies and techniques. However, the efficiency of some techniques will vary with intensity/effect (e.g. UV-light, ozone etc) applied and hence, some treatment efficiencies are presented with a rather large range.
It is important to keep in mind that high usage and release is not necessary the same as high environmental impact. Paracetamol is one of the most used compounds in all investigated countries, but 98% of paracetamol is metabolised, mainly to glucuronic acid (50–60% of the metabolites) and sulfuric acid (25–35%) in humans before excretion. There is little evidence that these metabolites would have a high environmental impact (Stuer-Lauridsen et al., 2000). However, there are pharmaceuticals with much lower volume consumed/year, but with a higher environmental impact potency, e.g. hormones such as contraceptives (ethinylestradiol and estradiol) (Adeel et al., 2017). Hence, waste water treatment efficiencies should be focused on those compounds and their metabolites.
Table 10. Top three pharmaceuticals sold (usage), excreted from humans (influent WWTP) and released into the aquatic environment (effluent WWTP) in Iceland, Norway and Sweden.
|Country||Usage||Influent WWTP||Effluent WWTP|
|Iceland||Paracetamol, codeine in combination with paracetamol, ibuprofen||Codeine (alone), paracetamol, valsartan||Codeine (alone), paracetamol, valsartan|
|Norway||Paracetamol, metformin, ibuprofen||Metformin, paracetamol, phenoxymethyl-|
|Metformin, tramadol, naproxen|
|Sweden||Paracetamol, lactulose, ibuprofen||Lactulose, carbamide, paracetamol||Lactulose, carbamide, chlorhexidine|
As Table 10 shows, paracetamol is the most commonly used pharmaceutical across the investigated Nordic countries. However, paracetamol is metabolised to a high degree in humans (Table S1) and hence, the most released compounds are other compounds from the sales list. Overall, there were only minor differences between the countries. There are measurements of released pharmaceuticals from WWTPs at Åland (and Sweden) available (Haikonen et al., 2018). The largest pharmaceutical releases in Åland were diclofenac (1,500 ng/dm3), metoprolol (1,300 ng/dm3), oxazepam (680 ng/dm3) and carbamazepine (640 ng/dm3).
Most studies available on treatment efficiency of pharmaceuticals in waste waters are focusing on removal of the parent compound, i.e. the distributed active compound. Hence, even with a reported high treatment efficiency, it might in reality be a high treatment efficiency of a small fraction of the compound, while the treatment efficiency on its metabolites remains unknown or not reported. Some metabolites might undergo deconjugation back to the parental compound in a WWTP such as e.g. tramadol (Douziech et al., 2018). These reactions are not well known and were outside the scope of the present report. Other compounds, such as e.g. diclofenac, are (to certain extent) used dermally and hence, the amount excreted and/or washed off unmetabolized are not known or estimated in the present report.
One source of pharmaceuticals into the environment that has not been considered is shipping, i.e. large cruise ships. Due to their capacity and amount of ships in certain popular regions, this might have a larger or comparable impact on the environment compared to the visited (small) village. However, the uncertainty factors are large, and this will need a thorough assessment which includes demography of passengers as well.
As can be seen from this report, the highest quantity of sold parental compounds are metabolised in the human body to a large extent, and they are excreted as metabolites. However, little is known about WWTP efficiency on metabolites or the impact of metabolites on the aquatic ecosystem.
Some compounds, e.g. tramadol may deconjugate from the metabolite and back to parental compound, and hence be released as a biologically active compound.
When environmental impacts are assessed, metabolites should be taken into account. Also, only the sales statistics does not provide enough information regarding which compounds that actually reach the aquatic environment. When WWTPs are planned and/or upgraded, pharmaceuticals and other environmental pollutants should be accounted for and suitable measurements to remove pharmaceuticals should be implemented when feasible.
We thank Guðrún Kr. Guðfinnsdóttir from the Directorate of Health, Iceland for providing data from the Medicines Registry. Íris Þórarinsdóttir and Hlöðver Stefán Þorgeirsson from Veitur Utilities PLC for providing information on wastewater treatment in Iceland. Rúnar Guðlaugsson from the Icelandic Medicines Agency for providing information on over the counter drug sales and Susanne Vävare from Government of Åland for contributing with data from Åland regarding WWTPs and pharmaceuticals. We also acknowledge the Swedish and Norwegian Medicines Agencies for providing relevant statistics.
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Table S1. Urinary/fecal excretion of chosen pharmaceuticals.
|ATC-code and name||% excreted of dose |
|Total excreted % |
|Metabolite names |
(only if easily available)
|A01AA01 - Sodium fluoride||69–83%||50% urine, 6–10% feces, 13–23% sweat||No metabolism||Applies to fluoride|
|A02BC01 - Omeprazol||<1%||76% urine, 17% feces||5-hydroxymethyl esomeprazole and its glucuronide, 5-carboxy esomepazole and its glucuronide|
|A02BC02 - Pantoprazole||<1%||71% urine, 18% feces||O-demethylated-pantoprazole-sulfate conjugate (major metabolite)||oxidized or reduced -S- of the o-demethylate-pantoprazole-sulfate conjugate|
|A02BC04 - Rabeprazol||90% urine, 10% feces||Primarily as thioether carboxylic acid; its glucuronide, and mercapturic acid metabolites|
|A02BC05 - Esomeprazole||<1%||76% in urine, 17% feces||5-hydroxymethyl esomeprazole and its glucuronide, 5-carboxy esomeprazole and its glucuronide|
|A10BA02 - Metformin||100%||52% urine||No metabolism||52% oral availability. No metabolism|
|A11CC05 – Chole-calciferol||intravenously inj: |
16% urine, 49% feces
|8 different glucuronide conjugates|
|B01AC06 - Acetylsalicylic acid||<1%||100% urine||5% salicylic acid and conjugates, 80% salicyluric acid, 10% salicylphenolic glucuronide, 5% salicylacyl glucuronide||di- and tri-hydroxy derivatives of salicylic acid (gentisic acid)|
|C01DA14 - Isosorbide mononitrate||2%||17% 2-glucuronide of parent, 37% isosorbide|
|C03CA01 - Furosemide||Intravenously (iv): 56%||Oral distribution: 55% urine and 35% feces.|
|iv: 83% in urine, 8% in feces||11% furosemide glucuronide, 2% norfurosemide|
|C07AB02 - Metoprolol||3%||99,6% in urine||60% demethylated acid derivative (I), 10% oxidative deaminated derivative (II), 10% aliphatic hydroxylated derivative (III)|
|C07AB03 - Atenolol||88%||39% urine, 36% feces||2% atenolol glucuronide, 2–3% hydroxyatenolol|
|C08CA01 - Amlodipine||<5%||59% urine, 23% feces||5.9% amlodipine dehydrogenation to M9. M9 underwent oxidative deamination to M10 (2.3%)||Seven minor metabolites|
|C09AA05 - Ramipril||<2%||56% urine, 39% feces||>10% ramiprilat + two diketopiperazine rearrangement products|
|C09CA01 - Losartan||4%||35% urine, 60% feces||6% carbolic acid metabolite (EXP3174), several sidechain hydroxy derivatives and N-glucuronides of both losartan and EXP3174|
|C09CA03 - Valsartan||9.8% urine, 71% feces||13% urine, 86% feces||9% valeryl-4-hydroxy valsartan|
|C09CA06 - Candesartan||26% candesartan||33% urine, 67% feces||26% candesartan, 8% O-desmethyl candesartan,||Applies to candesartan cilexetil, candesartan is a metabolite of cand. cil.|
|C10AA01 - Simvastatin||<1%||13% urine, 60% feces||Simvastatin acid (major form)||Other hydrolysis products|
|C10AA05 - Atorvastatin||<2%||<2% urine, mainly excreted in bile||O- and p-OH atorvastatin||All three are active and can excist in acid or lactone forms|
|G03AA07 – Ethinyl-estradiol||<1%||38% urine, 62% feces||Ethinylestradiol glucuronides and ethinylestradiol sulfate|
|G03AA07 – Levonorgestrel||20 to 67% urine, |
|Reduction, hydroxylation, and conjugation (glucuronidation and sulfation), Oxidation occurs primarily at the C2α and C16β positions, while reduction occurs in the A ring|
|G03CA03 - Estradiol||5.2–7.5%||54% urine, 6% feces||13–30% estrone glucuronide, 2-2.6–10.1% hydroxyestrone, 2.0–5.9% estriol, and 1.0–2.9% 16α-hydroxyestrone|
|H02AB06 - Prednisolone||16%||3,3% prednisone, 5,5% 20β-OH-prednisolone, 4,7% 6β OH-prednisolone, 3,5% 20α OH-prednisolone|
|H03AA01 – Levothyroxine sodium||50% urine, 11% feces||7% thyronine, 17% thyroacetic acid||T4 and conjug. T4, T3|
|J01CA08 - Pivmecillinam||60%||75% orally absorbed||N-formyl derivative. Hydrolysis of the β-lactam ring also occurs.|
|J01CE02 - Phenoxymethyl-|
|30%||49% urine, 32% feces||35–70% penicilloic acid||Hydroxylated metabolites||Good absorption after oral adm.|
|M01AB05 - Diclofenac||<1%||35-65% in urine, 15-45% feces||4-hydroxy diclofenac (major)|
|M01AE01 - Ibuprofen||1-9%||90% urine, 1% feces||35% 2-carboxy ibuprofen and conjugates, 26% 2-hydroxy ibuprofen and conjugates||30% 1-hydroxy ibuprofen, 3-hydroxy ibuprofen and conjugates||>90% oral availability|
|M01AE52 - Esomeprazole||<1%||76% urine, 17% feces||5-hydroxymethyl esomeprazole and its glucuronide, 5-carboxy esomepazole and its glucuronide|
|M01AE52 - Naproxen||10%||87% urine, 2.3% feces||60% conjugated naproxen, 5% O-desmethylnaproxen, 23% conjugated o-desmethylnaproxen||conjugated naproxen & O-desmethylnaproxen => naproxen during treatment.|
|M01AH01 - Celecoxib||2,60%||27% urine, 58% feces||20% carboxylic acid derivative|
|N02AJ06 – Codeine*||5–17%||95% urine||10–21% conjugated norcodeine, 5–13% conjugated morphine and 32–46% conjugated codeine||Trace of norcodeine and morphine|
|N02AX02 - Tramadol||29%||90% urine, 10% feces||20% O-desmethyltramadol and conjugated o-desmethyltramadol, 17% nortramadol, 20% free and conjugated o-desmethylnortramadol|
|N02BE01 – Paracetamol**||2%||85–90% urine||50-60% glucuronic acid, 25–35% sulfuric acid, 3% cysteine||4–5% methoxy acetaminophen|
|N05BA04 - Oxazepam||<1%||>66% in urine||61% oxazepam glucuronide, <5% as hydroxylated oxazepam|
|N05CF01 - Zopiclone||7-10%||80% urine||11% zopiclone-N-oxide, 15% N-desmethyl zopiclone||75–80% bioavailable oral|
|N06AB06 - Sertraline||<0,2%||40–45% urine||deaminated, hydroxylated and conjug. sertraline and norsertraline|
|N06AB10 - Escitalopram||8%||32% urine (citalopram)||10% N-desmethyl escitalopram||N,N-didesmethyl escitalopram|
|N06AX11 - Mirtazapine||75% urine, 25% feces||Quaternary ammonium glucuronide of mirtazapine and the glucuronides of 8-hydroxy-mirtazapine|
|N06AX16 - Venlafaxin||5%||87% urine||29–48% o-desmethyl venlafaxin, 0.2-7.4% norvenlafaxin, 6–19% di-norvenlafaxin|
|R01AA07 - Xylometazoline||Not known||Not known||Not known|
|R01AD09 - Mometasone||<1%||8% urine, 74% feces||6B-OH metabolite, dechlorinated metabolite||Extensively metabolized|
|R03AC02 - Salbutamol||28% after inhalation in urine, 32% in urine after oral intake.||Inhalation: 70% urine, 10% feces. Oral: |
75% urine, 4% feces.
|48% 4-O-sulfate conjugate (oral), 1–2% glucuronide salbutamol|
|R05DA01 - Ethylmorphine||4.4%||>76% urine||41% ethylmorphine-6-glucuronide, 12% morphine-3-glucuronide, 4.6% norethyl-morphine-6-glucuronide, 4.6% normorphine, 4.3% norethylmorphine, 3.3% morphine-6-glucuronide, 1.3% normorphine-glucuronide, 0.8% morphine|
|R06AA02 – Diphen-hydramine||1%||49–64% urine||N-acetyl-N-desmethyl diphenhydramine,diphenylmetoxyaceticacid. Both conjugated with glycine and glutamine.|
|R06AE07 - Cetirizine||5.8%||70% urine, 10% feces||O-dealkylated parent is the only identified metabolite|
|R06AX26 - Fexofenadine||95%||80% feces, 11% urine||No information available|
|R06AX27 - Desloratadine||1.7%||41% urine, 47% feces||22% conjugated 3-OH-desloratadine, |
5.7% conjugated di-OH-desloratadine
Note: References for data presented in this table are mainly Baselt (2017), Brunton et al. (2018) and Moffat et al., (2011). References for certain compounds are: Stanczyk et al. (2013); ethinylestradiol and estradiol, Shoupe and Haseltine (1993); levonogestrel and WHO (2006 and 2018); zopiclone. Numbers provided are the average estimate given in the references if nothing else is stated.
* In combinations with Paracetamol. Codeine figures here alone.
** Overdose of paracetamol: Higher levels of cysteine and mercapturic acid conjugates (up to 55%) and the parent compound (up to 14%).
Table S2. Summary of the WWTP efficiency literature survey.
|ATC code and compound||Treatment process||Treatment category||Removal efficiency (%)||Reference||Comments|
|N02BE01 - Paracetamol||Modified Bardenpho process||Secondary||99||Wang et al., 2016||Wastewater treatment plant, Bardenpho process is a continuous-flow suspended-growth process with alternating anoxic/aerobic/anoxic/aerobic and modified BP is BP with addition of an initial anaerobic zone.|
|Primary clarification + aerated tank with Fe(II)chloride precipitation||Primary+ secondary||>99||Ternes, 1998||German municipal sewage treatment plants/4 metabolites were measured in rivers and water streams.|
|Areated grit removal tank and sedimentation +CAS||Primary + secondary||Hospital 87 (Mean) Wastewater 97 (Mean)||Kosma et al., 2010||Municipal and hospital Wastewater treatment plant in Greece|
|Modelling||Secondary STP prediction||56||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|A10BA02 - Metformin||10 Different technologies used at 22 different WTP||From primary to secondary||40 (mean), 10–95 (25–75 percentile)||Greenham et al., 2019||Wastewater treatment plant (12 pharmaceuticals and 2 metabolites) paraxanthine (the primary metabolite of caffeine), and cotinine (the primary metabolite of nicotine)|
|of caffeine), and cotinine (the primary metabolite of nicotine).|
|93.8||Falås et al., 2012|
|8 WWTPs: all primary treatment, CAS with N removal, disinfection||Primary+ secondary||78–99||Kosma et al., 2015||8 Wastewater treatment plant in Greece/looked at Metformin and its bilogical transformation guanylurea.|
|Modelling||Secondary STP prediction||44||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|M01AE01 - Ibuprofen||Areated grit removal tank and sedimentation +CAS||Primary + secondary||Hospital 58 (Mean) Wastewater 77 (Mean)||Kosma et al., 2010||Municipal and hospital Wastewater treatment plant in Greece|
|Grit channels + primary clarifies + conventional activated sludge||Primary+ secondary||99.7||Wang et al., 2016||Wastewater treatment plant|
|Primary treatment + Orbal oxidation ditch + UV disinfection||Primary+ secondary+ tertiary||60-90||Wang et al., 2016||Wastewater treatment plant|
|CAS- wastewater treatment plant||Secondary||82.5||Radjenovic et al., 2007||Wastewater treatment plant|
|Primary clarification + aerated tank with Fe(II)chloride precipitation||Primary+ secondary||90 (ave)||Ternes, 1998||German municipal sewage treatment plants/4 metabolites were measured in rivers and water streams.|
|Modelling||Secondary STP prediction||52||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|Mean of 44 WWTPs in UK||Primary||17||Comber et al., 2019||Wastewater treatment plant|
|Mean of 44 WWTPs in UK||Primary + secondary||96||Comber et al., 2019||Wastewater treatment plant|
|B01AC06 – Acetyl-salicylic acid||Primary clarification + aerated tank with Fe(II)chloride precipitation||Primary+ secondary||81 (ave)||Ternes, 1998||German municipal sewage treatment plants/4 metabolites were measured in rivers and water streams.|
|Modified Bardenpho process||Secondary||92||Yu et al., 2013|
|Modelling||Secondary STP prediction||53||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|J01CE02 - Phenoxymethylpenicillin||Secondary STP prediction||67||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|M01AE52 - Naproxen||Modified Bardenpho process||Secondary||94||Wang et al., 2016||Wastewater treatment plant|
|Grit channels + primary clarifies + conventional activated sludge||Primary+ secondary||96.2||Wang et al., 2016||Wastewater treatment plant|
|CAS- wastewater treatment plant||Secondary||85.1||Radjenovic et al., 2007||Wastewater treatment plant|
|Primary clarification + aerated tank with Fe(II)chloride precipitation||Primary+ secondary||66 (ave)||Ternes, 1998||Wastewater treatment plant|
|Areated grit removal tank and sedimentation +CAS||Primary + secondary||Hospital 48 (Mean) Wastewater 62 (Mean)||Kosma et al., 2010||Municipal and hospital Wastewater treatment plant in Greece|
|Secondary STP prediction||58||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|C10AA05 - Atorvastatin||10 Different technologies used at 22 different WTP||From primary to secondary||50 (mean), -15–100 (25–75 percentile)||Greenham et al., 2019||Wastewater treatment plant (12 pharmaceuticals and 2 metabolites) paraxanthine (the primary metabolite of caffeine), and cotininee (the primary metabolite of nicotine).|
|CAS||Secondary||86||Ottmar et al., 2012||Synthetic wastewater|
|N02AX02 - Tramadol||Coarse bar, fine screen, grit and fat trap, primary clarifier, biological N removal and chemical precipitation||Primary + secondary||1 (+/- 10)||Gurke et al., 2015||Wastewater treatment plant/looked at metabolites as well|
|A02BC02 - Pantoprazole||CAS||Secondary||100 (found 0.1-0.02 µg/L in all effluent samples; <0.004 µg/L in influents)||Gracia-Lor et al., 2012||3 Wastewater treatment plants in Spain|
|C10AA01 - Simvastatin||CAS||Secondary||86||Ottmar et al., 2012||Synthetic wastewater|
|Secondary STP prediction||97||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|Pilot plants (Fixed bed bioreactor, bio filter (trickling filter), fluidized bed bio reactor)||Secondary||84-93||Ejhed et al., 2018||Pilot treatment plant|
|N02AJ06 - Codeine||Modelling||Secondary STP prediction||23||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|A02BC05 and M01AE52 -Esomeprazole||Primary treatment, CAS with N removal, disinfection||Primary+ secondary||>93||Kosma et al., 2017||Wastewater treatment plant, looked at metabolites|
|C09CA01 - Losartan||Bar screen, fat trap, CEPT (Bogota)||Advanced primary||8||Botero-Coy et al., 2018||Wastewater treatment plant|
|Bar screen, fat trap (Medellin)||Primary||35||Botero-Coy et al., 2018||Wastewater treatment plant|
|Coarse bar, fine screen, grit and fat trap, primary clarifier, biological N removal and chemical precipitation||Primary + secondary||55 (+/- 7)||Gurke et al., 2015||Wastewater treatment plant/looked at metabolites as well|
|M01AB05 - Diclofenac||Bardenpho process||Secondary||80||Wang et al., 2016||Wastewater treatment plant|
|Primary treatment +Orbal oxidation ditch+ UV disinfection||Primary+ secondary+ tertiary||10–60||Wang et al., 2016||Wastewater treatment plant|
|CAS- wastewater treatment plant||Secondary||50,1||Radjenovic et al., 2007||Wastewater treatment plant|
|Primary clarification + aerated tank with Fe(II)chloride precipitation||Primary+ secondary||69 (ave)||Ternes, 1998||German municipal sewage treatment plants/4 metabolites were measured in rivers and water streams.|
|Secondary STP prediction||30||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|Areated grit removal tank and sedimentation +CAS||Primary + Secondary||Hospital 15 (Mean) Wastewater 20 (Mean)||Kosma et al., 2010||Municipal and hospital Wastewater treatment plant in Greece|
|Mean of 44 WWTPs in UK||Primary||24||Comber et al., 2019||Wastewater treatment plant|
|Mean of 44 WWTPs in UK||Primary + secondary||48||Comber et al., 2019||Wastewater treatment plant|
|Mean of 44 WWTPs in UK||Primary + secondary + tertiary||56||Comber et al., 2019||Wastewater treatment plant|
|C09CA06 - Candesartan||Coarse bar, fine screen, grit and fat trap, primary clarifier, biological N removal and chemical precipitation||Primary + secondary||0 (+- 10)||Gurke et al., 2015||Wastewater treatment plant/looked at metabolites as well|
|N05BA04 - Oxazepam||Pilot plants (Fixed bed bioreactor, bio filter (trickling filter), fluidized bed bio reactor)||Secondary||-34–24||Ejhed et al., 2018||Pilot treatment plant|
|N06AB10 - Escitalopram||6 WWTPs: CAS, biofiltration (1 plant)||Tertiary||35–100||Silva et al., 2014||Wastewater treatment plants in portugal|
|2 WWTPs: CAS, trickling filter||Secondary||100||Silva et al., 2014||Wastewater treatment plants in Portugal|
|C08CA01 - Amlodipine||Pilot plants (Fixed bed bioreactor, bio filter (trickling filter), fluidized bed bio reactor)||Secondary||45–90||Ejhed et al., 2018||Pilot treatment plant|
|H02AB06 - Prednisolone||Pilot scale granular sludge MBR||Secondary||98.5||Zhao et al., 2014||Pilot experiment with synthetic wastewater|
|C09AA05 - Ramipril||10 Different technologies used at 22 different WTP||From primary to secondary||85 (mean), 70–100 (25–75 percentile)||Greenham et al., 2019||Wastewater treatment plant (12 pharmaceuticals and 2 metabolites) paraxanthine (the primary metabolite of caffeine), and cotinine (the primary metabolite of nicotine).|
|Primary + secondary||7 (+- 10)||Gurke et al., 2015||Wastewater treatment plant/looked at metabolites as well|
|Pilot plants (Fixed bed bioreactor, bio filter (trickling filter), fluidized bed bio reactor)||Secondary||43–87||Ejhed et al., 2018||Pilot treatment plant|
|C07AB02 - Metoprolol||Grit channels + primary clarifies + conventional activated sludge||Primary+ secondary||52.9||Roberts et al., 2016|
|CAS- wastewater treatment plant||Secondary||No removal||Radjenovic et al., 2007||Wastewater treatment plant|
|Primary clarification + aerated tank with Fe(II)chloride precipitation||Primary+ secondary||83 (ave)||Ternes, 1998||German municipal sewage treatment plants/4 metabolites were measured in rivers and water streams.|
|Secondary STP prediction||42||Khan and Ongerth, 2004||Wastewater treatment plant/modeling/looked at Metabolites as well|
|G03AA07 - Ethinylestradiol||Mean of 44 WWTPs in UK||Primary||4||Comber et al., 2019||Wastewater treatment plant|
|Mean of 44 WWTPs in UK||Primary + secondary||46||Comber et al., 2019||Wastewater treatment plant|
|Mean of 44 WWTPs in UK||Primary + secondary + tertiary||51||Comber et al., 2019||Wastewater treatment plant|
|Pilot plants (Fixed bed bioreactor, bio filter (trickling filter), fluidized bed bio reactor)||Secondary||69–89||Ejhed et al., 2018||Pilot treatment plant|
|G03CA03 - Estradiol||Mean of 44 WWTPs in UK||Primary||3||Comber et al., 2019||Wastewater treatment plant|
|Mean of 44 WWTPs in UK||Primary + secondary||89||Comber et al., 2019||Wastewater treatment plant|
|Mean of 44 WWTPs in UK||Primary + secondary + tertiary||95||Comber et al., 2019||Wastewater treatment plant|
|Pilot plants (Fixed bed bioreactor, bio filter (trickling filter), fluidized bed bio reactor)||Secondary||41–99||Ejhed et al., 2018||Pilot treatment plant|
Note: References for the table: (Botero-Coy et al., 2018; Comber et al., 2018; Ejhed et al., 2018; Falås et al., 2012; Gracia-Lor et al., 2012; Greenham et al., 2019; Gurke et al., 2015; Khan and Ongerth, 2004; Ottmar et al., 2012; Radjenovic et al., 2007; Silva et al., 2014; Ternes, 1998; Wang and Wang, 2016)
Kosma et al., 2015, Roberts et al., 2016, Yu et al., 2013, Zhao et al., 2014.
Table S3. Total amount of the forty largest drug groups (ATC code 5th level) in Iceland 2017, and total amount sold.
|ATC index||Active substance||mg/DDD||DDD - Nationwide||Total kg sold per year - Nationwide|
|N02AJ06||Codeine and paracetamol||100 + 3,000||2,392,907||7,418|
|C09DA01||Losartan and diuretics||50||4,327,616||216|
|C09DA03||Valsartan and diuretics||80||1,582,042||127|
|C03EA01||Hydrochlorothiazide and potassium-sparing agents||25||3,241,900||81|
|G03AA07||levonorgestrel and ethinylestradiol||1.5 + 0.025||2,715,552||4|
Pernilla Carlsson (NIVA), Kristín Ólafsdottir (Háskóli Íslands), Arndís Sue-Ching Löve (Háskóli Íslands), Christian Vogelsang (NIVA), Mona Eftekhar Dadkhah (NIVA), Johan Lindberg (RISE) and Malcolm Reid (NIVA)
ISBN 978-92-893-6497-3 (PDF)
ISBN 978-92-893-6498-0 (ONLINE)
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