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A review on emerging contaminants in wastewaters and the environment: Current knowledge, understudied areas and recommendations for future monitoring

Bruce Petrie a, Ruth Barden b, Barbara Kasprzyk-Hordern a,*

a Department of Chemistry, University of Bath, Bath BA2 7AY, UK b Wessex Water, Bath BA2 7WW, UK

a r t i c l e i n f o

Article history:

Received 31 May 2014

Received in revised form

26 August 2014

Accepted 28 August 2014

Available online 10 September 2014

Keywords:

Micropollutant

River

Biosolids

Pharmaceutical

Chiral

Wastewater

* Corresponding author. Tel.: þ44 (0)1225 385 E-mail address: b.kasprzyk-hordern@bath

http://dx.doi.org/10.1016/j.watres.2014.08.053 0043-1354/© 2014 The Authors. Published by org/licenses/by/3.0/).

a b s t r a c t

This review identifies understudied areas of emerging contaminant (EC) research in waste-

waters and the environment, and recommends direction for future monitoring. Non-

regulated trace organic ECs including pharmaceuticals, illicit drugs and personal care

products are focused on due to ongoing policy initiatives and the expectant broadening of

environmental legislation. These ECs are ubiquitous in the aquatic environment, mainly

derived from the discharge of municipal wastewater effluents. Their presence is of concern

due to the possible ecological impact (e.g., endocrine disruption) to biota within the envi-

ronment. To better understand their fate in wastewaters and in the environment, a stand-

ardised approach to sampling is needed. This ensures representative data is attained and

facilitates a better understanding of spatial and temporal trends of EC occurrence. During

wastewater treatment, there is a lack of suspended particulate matter analysis due to further

preparation requirements and a lack of good analytical approaches. This results in the

under-reporting of several ECs entering wastewater treatment works (WwTWs) and the

aquatic environment. Also, sludge can act as a concentrating medium for some chemicals

during wastewater treatment. The majority of treated sludge is applied directly to agricul-

tural land without analysis for ECs. As a result there is a paucity of information on the fate of

ECs in soils and consequently, there has been no driver to investigate the toxicity to exposed

terrestrial organisms. Therefore a more holistic approach to environmental monitoring is

required, such that the fate and impact of ECs in all exposed environmental compartments

are studied. The traditional analytical approach of applying targeted screening with low

resolution mass spectrometry (e.g., triple quadrupoles) results in numerous chemicals such

as transformation products going undetected. These can exhibit similar toxicity to the parent

EC, demonstrating the necessity of using an integrated analytical approach which compli-

ments targeted and non-targeted screening with biological assays to measure ecological

impact. With respect to current toxicity testing protocols, failure to consider the enantio-

meric distribution of chiral compounds found in the environment, and the possible toxico-

logical differences between enantiomers is concerning. Such information is essential for the

development of more accurate environmental risk assessment.

© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC

BY license (http://creativecommons.org/licenses/by/3.0/).

013; fax: þ44 (0)1225 386231. .ac.uk (B. Kasprzyk-Hordern).

Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 74

1. Introduction

In surface waters, a broad range of inorganic and organic

contaminants are controlled by legislation outlined by the

European Commission (European Commission, 2008). These

have traditionally been industrial or agricultural chemicals.

However, legislation is expected to broaden to encompass a

greater number of municipal derived chemicals described as

ECs following the recent proposal of the pharmaceuticals

17b-estradiol (E2), 17a-ethinylestradiol (EE2) and diclofenac as

priority hazardous substances (European Commission, 2012).

Proposed legislative targets for consent were 0.4, 0.035 and

100 ng l�1 for E2, EE2 and diclofenac, respectively (European Commission, 2012). Also, dissemination of antibiotic resis-

tant bacteria in the environment caused by the presence of

antibacterial drugs is an emerging concern (Marti et al., 2014).

This emphasises the level of concern posed by this family of

environmental pollutants. Large numbers of non-regulated

ECs have been observed mainly in the ng to mg l�1 range in surface waters throughout the UK (Ashton et al., 2004;

Roberts and Thomas, 2006; Kasprzyk-Hordern et al., 2007,

2008a,b, 2009; Baker and Kasprzyk-Hordern, 2011a, 2013) and

across the rest of Europe (Gracia-Lor et al., 2011; Gros et al.,

2012; Kunkel and Radke, 2012; Ferrando-Climent et al., 2013;

Fenech et al., 2013; Hughes et al., 2013; Loos et al., 2013;

L�opez-Serna et al., 2013). To date, >200 different pharma- ceuticals alone have been reported in river waters globally,

with concentrations up to a maximum of 6.5 mg l�1 for the antibiotic ciprofloxacin (Hughes et al., 2013). Single com-

pound acute toxicity testing (including crustaceans, algae

and bacteria) conducted under controlled laboratory condi-

tions have found median effective concentrations (EC50's e concentration at which the toxicological response to an or-

ganism is halfway between a normal and maximum

response for a pre-set time period) for a number of these ECs

to be < 1 mg l�1 (Bruce and Versteeg, 1992; Holten Lützhøft et al., 1999; Halling-Sørensen, 2000; Brooks et al., 2003;

Andreozzi et al., 2004; Brain et al., 2004; Eguchi et al., 2004;

Cleuvers, 2005; Isidori et al., 2005a,b; DellaGreca et al., 2007;

Isidori et al., 2007; DeLorenzo and Fleming, 2008; Terasaki

et al., 2009; Giudice and Young, 2010). Such effect concen-

trations classify the chemical as potentially very toxic to

aquatic organisms as described under the EU-Directive 93/67/

EEC (Commission of the European Communities, 1996;

Cleuvers, 2003). The presence of these chemicals in the

environment is more concerning considering that they do not

appear individually, but as a complex mixture, which could

lead to unwanted synergistic effects. The ubiquity of a high

number of potentially toxic ECs in the environment un-

derpins the need to better understand their occurrence, fate

and ecological impact.

This review describes current knowledge on the occur-

rence of ECs in wastewaters and surface waters using the UK

data set as an example. From the data set and wider literature,

areas of concern considered to be understudied are discussed.

Among them are: spatial and temporal variability of ECs in

wastewater and river water, partitioning of ECs to solid matter

during wastewater treatment, fate of ECs in environmental

waters and toxicological impact of ECs within the

environment. Finally, recommendation for future environ-

mental monitoring approaches is proposed.

2. Current knowledge of EC occurrence in wastewaters and surface waters

The presence of ECs in the environment is mainly attributed

to the discharge of treated wastewater from WwTWs. Con-

ventional secondary processes (activated sludge and trickling

filters) represent the most extensively used and studied pro-

cesses and are therefore focused on in this review. However,

these processes are not designed to remove ECs resulting in

their discharge to receiving surface waters including rivers,

lakes and coastal waters. A UK data set is used as a baseline to

outline current knowledge of EC contamination within

wastewaters and surface waters for a European country.

Approximately 70 pharmaceuticals belonging to a variety

of therapeutic classes have been reported in UK environ-

mental waters (Table 1). The most studied are non-steroidal

anti-inflammatory drugs (NSAIDs), b-blockers, anti-

depressants and the antiepileptic carbamazepine. These are

highly prescribed (>1000 kg per annum) and ubiquitous to influent wastewaters. Removal varies broadly from low (<50%) to high (>80%) due to their different physicochemical prop- erties and susceptibility to biological attack (Tadkaew et al.,

2011; Petrie et al., 2013a). Incomplete removal results in

pharmaceuticals being reported in receiving surface waters in

the ng to mg l�1 range. The analgesic tramadol has been observed in river water at the highest concentration up to a

maximum of 7731 ng l�1 (Kasprzyk-Hordern et al., 2008b). To date, a total of 15 illicit drugs and licit stimulants have been

reported in UK wastewaters (Table 1). Due to the lower con-

sumption of illicit drugs within the community, concentra-

tions observed in influent wastewaters are expectantly lower.

Nevertheless, incomplete removal during wastewater treat-

ment has resulted in several of these ECs being found in final

effluents and receiving surface waters. The hallucinogen 3,4-

methylenedioxy-N-methylamphetamine (MDMA) and the

stimulant cocaine have been observed in river water at con-

centrations of 25 and 17 ng l�1, respectively (Baker and

Kasprzyk-Hordern, 2011a,b, 2013). In contrast, antimicro-

bials, sunscreen agents and preservatives are high usage

chemicals due to their application in a broad range of personal

care products. These types of chemical are often observed in

influent wastewater at >1000 ng l�1 (Table 1). Removals during wastewater treatment are generally good and range from 50 to

100%. However, notable concentrations remain in final efflu-

ents owing to the relatively high influent concentrations

encountered. The sunscreen agent 4-benzophenone has been

observed at mean final effluent concentrations ranging from

3597 to 5790 ng l�1 (Kasprzyk-Hordern et al., 2008a, 2009). The data set also revealed that the majority of EC analysis is un-

dertaken on the aqueous phase (i.e., a pre-filtered sample)

only, and there has been a lack of particulate phase mea-

surements made (e.g., sludge or suspended particulate mat-

ter). The observations of the UK data set are typical of EC

research reported throughout the rest of Europe (Gracia-Lor

et al., 2011; Gros et al., 2012; Ferrando-Climent et al., 2013;

Table 1 e Emerging contaminant occurrence information for wastewaters and surface waters in the UK (all data reported as mean concentrations and aqueous phases unless otherwise stated).

Emerging contaminant

Family/use Prescription 2012 (kg)a

Excretion unchanged (%)

Known metabolites Influent (ng l�1)

Removal (%)

Sludge (ng kg�1)

Final effluent (ng l�1)

Surface water (ng l�1)

Surface water max. (ng l�1)

Pharmaceuticals

Estrone Steroid estrogen e e Sulfate and glucuronide

conjugates

49c L-Hc,m e 4.3e12b,m e e

17b-estradiol Steroid estrogen 84 e Sulfate and glucuronide

conjugates

20c M-Hc,m e 0.4e1.3b,m e e

17a-ethinylestradiol Synthetic estrogen 12 e Sulfate and glucuronide

conjugates

1.0c L-Hc,m e 0.20e0.47b,m e e

Propranolol Beta blocker 9076 <0.5d 4-Hydroxypropranolol (active),

glucuronide conjugates

(20%)f

60e638c,e,g,j L-Hc,g,j 170c 93e388b,e,g,j,k <0.5e107deg,j,k 215k

Metoprolol Beta blocker 2311 10e30d No active metabolitesf 75e110e,g L-Mg e 41e69e,g <0.5e10deg 12f

Sulbutamol Beta blocker 182 30d Sulfate conjugate 0.1e130e,g M-Hg e 63e66e,g <0.5-2eeg 8f

Atenolol Beta blocker 20,725 50d Hydroxylated

metabolite (3%)f 12,913e14,223e,g M-Hg e 2123e2,870e,g <1e487deg 560f

Carbamazepine Antiepileptic 44,498 3d Hydroxylated

(10,11-epoxide)

(active), conjugated

metabolitesf

950e2,593e,g Lg 826e3,117e,g <0.5e251deg 684f

Gabapentin Antiepileptic 104,110 100d e 15,034e18,474e,g L-Hg 2592e21,417e,g <0.6e1,879deg 1,887f

Acetaminophen NSAID >2,000,000 20d Sulfate conjugate (30%), paracetmaol

cysteinate,

mercapturate (5%)f

6924e492,340e,g,j Hg,j e <20e11,733e,g,j <1.5e1,388deg 2,382f

Diclofenac NSAID 10,652 5e10f Glucuronide,

sulfate conjugatesf 69e1,500c,e,g,j L-Hc,g,j 70c 58e599b,e,g,j,k <0.5e154eeg,k 568k

Ibuprofen NSAID 108,435 1f (þ)-2-40-(2-Hydroxy-2- methylpropyl)-

phenylpropionic

acid (25%) and

(þ)-2-40-(2-carboxypropyl)- phenylpropionic

acid (37%),

conjugated

ibuprofen (14%)f

1681e33,764c,e,g,j Hc,g 380c 143e4,239b,e,g,j,k 1e2,370eeg,j,k 5,044k

Naproxen NSAID 126,258 <1 6-o-Desmethyl naproxen (<1%), conjugates (66e92%)f

838e1,173g Mg e 170e370g 1e59f,g 146f

Ketoprofen NSAID 243 0e50 2-(3-benzoylphenyl)-

propanoic acid and

glucuronides

28e102e,g Mg e 16e23e,g 1e4eeg 14f

Clofibric acid Metabolite e e e 1e651e,g,j L-Hg,j e 6e44e,g,j <0.3e101eeg 164f

Salicylic acid Metabolite e e e 5866e52,000c,e,g L-Hc,g e 75e209e 4e62eeg 302f

(continued on next page)

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Table 1 e (continued)

Emerging contaminant

Family/use Prescription 2012 (kg)a

Excretion unchanged (%)

Known metabolites Influent (ng l�1)

Removal (%)

Sludge (ng kg�1)

Final effluent (ng l�1)

Surface water (ng l�1)

Surface water max. (ng l�1)

Ranitidine H2 receptor

agonist

35,665 30d N-oxide (3e6%),

S-oxide (1e2%)

desmethyl

ranitidine (1e2%)f

<12e5,060e,g L-Hg e <9e425e,g <3e32deg 73f

Cimetidine H2 receptor

agonist

3195 48e75d Cimetidine

N-glucuronide (24%),

cimetidine

suphoxide (7e14%),

hydroxymethylcimetidine

(4%)f

2219e3,452e,g L-Mg e 462e2,605e,g <0.5e105eeg 220f

Furosemide Diuretic 14,840 Littlef Mainly glucuronidesf 1476e2,789e,g L-Mg e 629e1,161e,g <6e129eeg 630f

Bezafibrate Lipid regulator 7966 50f Glucuronides (20%)f 420e971e,g L-Mg e 177e418e,g <10e60eeg 90g

Simvastatin Lipid regulator 49,198 Little b-Hydroxyacid metabolitef <7e115e,g e e <3e5e,g <0.6d,e,g e Fluoxetine Antidepressant 5319 11l Norfluoxetine 14e86c,h,i,l L-Hc,i 170c 16e29b,h,i 5.8e14h,i 14h,i

Norfluoxetine Metabolite e e e 3.3e63h,i,l Mi e 5.8e13h,i 1.3e2.8h,i 3.5i

Venlafaxine Antidepressant 16,211 5l Desmethylvenlafaxine 120e249h,i,l Li e 95e188h,i 1.1e35h,i 85i

Dosulepin Antidepressant 3270 11l Desmethyldosulepin 21e228h,i,l M-Hi e 57h 0.5e25h,i 32h,i

Amitriptyline Antidepressant 10,171 2l Nortriptyline,

10-hydroxyamitriptyline

(active),

10-Hydroxynortriptyline

(active)f

106e2,092e,g,h,i,l M-Hg,i e 66e207eei <0.5e30dei 72h

Nortriptyline Antidepressant 439 3l Hydroxynortriptyline 5.1e114h,i,l Mi e 7.6e33h,i 0.8e6.8h,i 19h,i

Valsartan Hypertension 6484 80d Valeryl 4-hydroxy valsartanf 342e1,734e,g L-Hg e 192e344e,g <1e55deg 144f

Diltiazem Calcium-channel

blocker

21,922 2e4f Desacetyldiltiazem,

N-monodemethyldiltiazem

(active)f

770e1,559e,g L-Mg e 95e357e,g <1e17deg 65f

Theophylline Bronchodilator 4l 10 Caffeine, 3-methylxanthine 9467e20,400h,i,l Hi e 1220e3,169h,i 76e558h,i 1,439i

Tramadol Analgesic 41,445 15e35d,l Desmethyltramadol

(active)f 733e48,488eei,l Lg,i e 739e59,046e,g,h,i <30e5,970def,h,i 7,731f

Nortramadol Metabolite e e e 226e2,457h,i,l M-Hi e 145e433h,i 11e181h,i 410i

Codeine Various 34,626 64e70d,l Codeine-6-glucuronide

(main),

free/conjugated morphine

(10e15%), norcodeine

(10e20%)f

1088e10,321eei,l L-Hg,i e 372e5,271e,gei <1.5e347dei 815f

Norcodeine Metabolite e e e 30e112h,i,l Mi e 24e33h,i 2.1e9.0h,i 20h

Oxycocodone Analgesic e 9l Noroxycocodone,

oxymorphone,

5.0e12h,i,l Li e 7e12h,i 0.5e3h,i 7.1h

Oxymorphone Analgesic e 11l Noroxycodone,

noroxymorphone

11e20h,i,l e e <1.7e8.4h,i <0.1e2.3h,i 3.5i

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Morphine Analgesic 5684 55l Morphine-3-glucuronide,

morphine-6-glucuronide,

normorphine

340e481h,i,l Hi e 59e131h,i 1.6e36h,i 36h

Normorphine Metabolite e e e 51e203h,i,l Hi e 20e62h,i <5e5.7h,i 5.7i

Dihydrocodeine Analgesic 9720 54l Dihydromorphine

(active)

227e386h,i,l Li e 118e146h,i 2.9e36h,i 97i

Buprenorphine Various 91 1l Norbuprenorphine and

glucuronides

33e47h,i e e 14h <0.5e15h,i 14i

Norbuprenorphine Metabolite e e e <1e19h,i e e <0.7e7.5h,i <0.5e12.2h,i 3.4i

Methadone Analgesic 1687 28l EDDP, EMDP 52e88h,i,l Li e 42e50h,i 0.6e12h,i 24i

EDDP Metabolite e e e 71e193h,i,l L-Mi e 32e89h,i 1.2e19h,i 38h

EMDP Metabolite e e e 1.8e5.7h,i e e 1.3e1.7h,i 0.6e1.0h,i 1.1h

Fentanyl Analgesic 1.0 3l Norfentanyl,

despropionylfentanyl

1.3e1.7h,i e e <0.1e0.5h,i <0.1h e

Norfentanyl Metabolite e e e 6.9h e e 1.1h <0.1h e Propoxyphene Analgesic e 1l Norproxyphene 8.2e11h,i e e 7.1h <0.1h e Norpropoxyphene Metabolite e e e <4.2e184h,i,l Li e 91e106h,i 6.5e31h,i 80i

Temazepam Hypnotic 833 75l Oxazepam 85e208h,i,l Li e 135e179h,i 3.2e34h,i 78i

Diazepam Hypnotic 335 Tracel Nordiazepam,

temazepam,

oxazepam

<0.9e7.6h,i e e 1.6e5.1h,i 0.6e0.9h,i 1.1h,i

Nordiazepam Metabolite e e e 12e25h,i,l Mi e 5.8e9.9h,i 0.7e3.2h,i 6.8i

7-Aminonitrazepam Metabolite e e e <1.9e205h,i e e <1.2h <0.5h e Oxazepam Hypnotic 85 33l Glucuronide metabolite 22e50h,i,l Li e 33e58h,i 2.4e11h,i 21i

Ketamine Anaesthetic 64 2l Norketamine 52e235h,i,l Li e 83e130h,i 0.6e27h,i 54i

Norketamine Metabolite e e e 11e85h,i,l Li e 14e28h,i 1.7e5.8h,i 14h

Sildenafil Erectile

dysfunction

570 <1 N-desmethyl sildenafil 8.3e25h,i,l Li 7.0e9.7h,i <1e2.2h,i 2.9i

Ephedrine/

pseudoephedrine

Various 622 40e90 Cathine 476e966h,i,l Hi e 35e70h,i 7.7e15h,i 17e29h,i

Norephedrine Various e 80e90 e 40e86h,i Hi e 59h <5h e Amoxicillin Antibacterial 158,231 30e70 Amoxicilloic acid <87e e e 31e <2.5e245d,e,f 622f

Erythromycin Antibacterial 41,057 5d Erythromycin-H2O f 71e2,530c,e,g,j L-Hc,g,j 50c 109e1,385b,e,g,j,k <0.5e159deg,j,k 1,022k

Metronidazole Antibacterial 12,300 20d 1-(b-hydroxymethyl-

5-nitroimidazole,

2-methyl-5-

nitroimidazole-1yl-

acetic

acidf

569e2,608e,g Lg e 265e373e,g <1.5e12deg 24f

Ofloxacin Antibacterial 219 65e80 Desmethyl, N-oxide

metabolites

180c M-Hc 210c 10b e e

Chloramphenicol Antibacterial e 8e12d Glucuronide

conjugatesf <4e248e,g Hg e <6e21e,g <10d,f,g 40f

Sulfamethoxazole Antibacterial e 30d N4-acetylated

metabolite

<3e115e,g L-Mg e 10e19e,g <0.5e2deg 8g

Sulfapyridine Antibacterial e <10 Hydroxyl, acetyl metabolites

914e4,971g L-Hg e 277e455g <2e28f,g 142f

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Table 1 e (continued)

Emerging contaminant

Family/use Prescription 2012 (kg)a

Excretion unchanged (%)

Known metabolites Influent (ng l�1)

Removal (%)

Slud (ng kg

Final effluent (ng l�1)

Surface water (ng l�1)

Surface water max. (ng l�1)

Sulfasalazine Chronic

bowel

disorders

54,039 15 5-Aminosalicylic acid

(active),

sulfapyridine (active)f

0.2e116e,g Lg e 0.3e484e,g <1.5e76eeg 168f

Trimethoprim Antibacterial 10,998 80d 1,3-oxides, 30 4-hydroxy derivativesf

213e2,925e,g,j L-Mj,g e 128e1,152e,g,j,k <1.5e108deg,j,k 183f

Oxytetracycline Antibacterial 17,143 30 N-desmethyloxytetracycline 3600 Hc 6710 170b e e

Tamoxifen Anti-cancer 453 30 Hydroxytamoxifen (active) 143e215j Lj e <10e369j,k <10e212j,k e Illicit drugs and licit stimulants

MDMA Hallucinogen e 20l MDA, HMMA, HMA 10e231h,i,l Li 13e38h,i 0.5e8.7h,i 25h,i

MDEA Hallucinogen e 19l MDA <0.3e1.4h,i e e <0.5h <0.1h e MDA Hallucinogen/

metabolite

e Highl e 10e18h,i,l e e 11e15h,i <0.1e1.7h,i 1.7i

Amphetamine Stimulant e 1e74d,l 25% Phenylacetone,

benzoic acid,

hippuric acid, <10% 4-hydroxy-norephedrine,

norephedrinef

77e5,236eei,l Hg,i e 2e201e,gei <1e9dei 4.3h

Methamphetamine Stimulant e 43l Amphetamine,

4-hydroxymethamphetamine

2e40h,i,l M-Hi e 0.8e1h,i <0.1e0.3h,i 0.3i

Cocaine Stimulant e 1e9f,l 35e54% benzoylecgonine,

32e49% ecgoninef

methyl ester (main)

57e526e,gei,l L-Hi e <1e149e,gei <0.3e6dei 17i

Benzoylecgonine Metabolite e e e 196e1,544e,gei,l L-Hg e 13e1,597e,gei <1e92dei 72i

Norbenzoylecgonine Metabolite e 1l e 6e54h,i,l e e 3.6e7h,i 0.3e2h,i 3.1i

Norcocaine Metabolite e e e 1h e e <0.2h 0.1h 0.1h

Cocaethylene Metabolite e e e 3e45h,i,l L-Mi e 1.7e3.8h,i 0.1e0.6h,i 1.4h

Benzylpiperazine Stimulant e 7(rat)l Hydroxylated metabolite 22e25h,i L-Hi e 31e44h,i 1.1e26h,i 65h

Trifluoromethyl-

phenylpiperazine

Stimulant e <1(rat)l Hydroxylated metabolite 2.4e5.1h,i,l e e 4.6e6.6h,i 0.2e1.2h,i 6.1i

6-acetylmorphine Metabolite e <1l e 3.0e22h,i,l e e <0.3e2.2h,i <0.1h e Caffeine Human indicator e 1l Acids and 1-methylxanthine 9902e25,138h,i,l Hi e 1744e2,048h,i 163e743h,i 1,716i

Nicotine Human indicator 442 e e 3919e9,684h,i,l Hi e 52h 12e86h,i 148i

Personal care products

Triclosan Antibacterial e e e 70e2,500c,e,g M-Hc,g 5,14 25e200b,e,g 5e48eeg e

Bisphenol A Plasticizer e e e 416e2,050c,e,g M-Hc,g 320c 35e86b,e,g <6e34eeg e 1-benzophenone Sunscreen agent e e e 134e306e,g Hg e 12e32e,g <0.3e9e,f e 2-benzophenone Sunscreen agent e e e 25e194e,g Hg e 1e4e,g <0.5e18eeg e 3-benzophenone Sunscreen agent e e e 638e1,195e,g M-Hg e 22e231e,g <15e36eeg e 4-benzophenone Sunscreen agent e e e 3597e5,790e,g Lg e 2701e4,309e,g <3e227eeg e Methylparaben Preservative e e e 2642e11,601e,g Hg e <3e50e,g <0.3e68eeg e Ethylparaben Preservative e e e 589e2,002e,g Hg e 4e50e,g 1e13eeg e

Propylparaben Preservative e e e 598e3,090e,g Hg e 26e63e,g <0.2e7eeg e Butylparaben Preservative e e e 50e723e,g Hg e <1e,g <0.3e6eeg e

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re m o v a l is

w it h in

th e ra n g e 5 0 e 8 0 % ; H

s e c o n d a ry

re m o v a l > 8 0 % .

a T a k e n fr o m

N H S P re s c ri p ti o n C o s t A n a ly s is

in fo rm

a ti o n fo r E n g la n d (N

a ti o n a l H e a lt h S e rv ic e , 2 0 1 2 ).

b M e d ia n d a ta

re p o rt e d fr o m

1 6 2 W

w T W

s e

G a rd

n e r e t a l. , 2 0 1 2 .

c D a ta

o b ta in e d fr o m

1 4 W

w T W

s (i n c . a c ti v a te d s lu d g e , tr ic k li n g fi lt e rs , m e m b ra n e b io re a c to rs

a n d b io lo g ic a l n u tr ie n t re m o v a l p la n ts

e G a rd

n e r e t a l. , 2 0 1 3 .

d K a s p rz y k -H

o rd

e rn

e t a l. , 2 0 0 7 .

e K a s p rz y k -H

o rd

e rn

e t a l. , 2 0 0 8 a .

f K a s p rz y k -H

o rd

e rn

e t a l. , 2 0 0 8 b .

g T w o W

w T W

s m o n it o re d (a c ti v a te d s lu d g e a n d tr ic k li n g fi lt e r)

K a s p rz y k -H

o rd

e rn

e t a l. , 2 0 0 9 .

h B a k e r a n d K a s p rz y k -H

o rd

e rn

, 2 0 1 1 a .

i S e v e n W

w T W

s (a c ti v a te d s lu d g e a n d tr ic k li n g fi lt e rs ), m e d ia n d a ta

re p o rt e d e

B a k e r a n d K a s p rz y k -H

o rd

e rn

, 2 0 1 3 .

j W

w T W

s is

a tr a in

o f p ro

c e s s e s (t ri c k li n g fi lt e r,

a c ti v a te d s lu d g e a n d U V

tr e a tm

e n t)

R o b e rt s a n d T h o m a s , 2 0 0 6 .

k F iv e W

w T W

s (o x id a ti o n d it c h , a c ti v a te d s lu d g e a n d tr ic k li n g fi lt e rs ) A s h to n e t a l. , 2 0 0 4 .

l B a k e r e t a l. , 2 0 1 4 .

m In c lu d e s p a rt ic u la te

c o n c e n tr a ti o n s e

K o h e t a l. , 2 0 0 9 .

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 7 9

Fenech et al., 2013; Hughes et al., 2013; Loos et al., 2013; L�opez-

Serna et al., 2013).

3. Understudied areas of EC pollution in wastewaters and the environment

The data set described (Table 1), and wider literature was used

to identify key areas of concern considered to be under-

studied. These are discussed to address deficiencies in current

knowledge of EC contamination in wastewaters and the

environment.

3.1. Spatial and temporal variability of ECs in wastewater and river water

3.1.1. Factors which influence receiving wastewater concentration A large variation in influent wastewater concentrations

(equivalent to more than an order of magnitude) has been

observed for some ECs (Table 1). For example, acetaminophen

was found within influent wastewater at mean concentra-

tions ranging from 6924 to 492,340 ng l�1 (Roberts and Thomas, 2006; Kasprzyk-Hordern et al., 2008a, 2009) (Table 1). This in-

dicates spatial and/or temporal variations in their usage.

However dilution from industrial inputs, degradation within

the upstream sewer, rainfall and sampling mode will all

contribute to this variability. The use of inappropriate sam-

pling strategies is considered the greatest weakness of re-

ported occurrence data. Ort et al. (2010a,b) gave an excellent

account of uncertainties associated with different sampling

modes. Current approaches tend to use discrete grab sampling

of low inter-day frequency and often no intra-day repetition.

To demonstrate, all reported data in Table 1 was obtained

using a grab sampling approach. This approach has limita-

tions as it only yields a snap shot of EC concentration for a

specific point in time. Time and volume/flow proportional

composite samplers are also used (Pl�osz et al., 2010; Coutu

et al., 2013; L�opez-Serna et al., 2013), but to a lesser extent.

The latter being preferred as it accounts for fluctuations in

flow. The relatively high cost and logistical issues associated

with this sampling mode has resulted in very few studies

applying flow proportional sampling. Also, a major uncer-

tainty of 24 h composite sampling is chemical stability. This is

not often investigated but is known to be significant for some

chemicals (Baker and Kasprzyk-Hordern, 2013). Due to un-

certainties of existing sampling methods, there is lack of un-

derstanding on spatial and temporal variations in EC

concentrations.

3.1.2. Spatial distribution Assessing spatial distribution of EC contamination is notori-

ously difficult. The collation and interpretation of literature

data from a variety of sources has obvious limitations. On the

other hand, studies have been able to tentatively assess

spatial distribution within a single catchment (Vazquez-Roig

et al., 2012; Baker and Kasprzyk-Hordern, 2013). Such studies

have to rely heavily on discrete grab sampling due to the large

number of sites which are monitored at approximately the

same time. Despite the uncertainties associated with grab

Fig. 1 e Fluctuations of mass flux for selected antibiotics in wastewater throughout a one day period (a) and during the

course of a year (b) e adapted from Coutu et al. (2013).

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 710

sampling, information attained from such studies is

extremely valuable. It can be used as a primary indicator of

sites within the catchment that require more detailed study.

These can then be subject to more robust sampling protocols

to facilitate greater understanding of EC fate and occurrence

during wastewater treatment and in the environment at these

predetermined sites of interest.

3.1.3. Intra-day variation It is anticipated that concentrations of ECs in receiving

wastewater vary throughout the day. Coutu et al. (2013)

collected hourly composite samples (i.e., one sample every

15 min to create an hourly composite) for influent wastewater

over 24 h sampling periods. This was used to investigate

within day variability of antibiotic concentrations. Samples

were chilled upon collection but their stability at 4 �C was not investigated or referenced to. However, significant degrada-

tion (>15%) has been observed for some ECs stored at 4 �C for only 12 h (Baker and Kasprzyk-Hordern, 2013). Despite this

uncertainty, the six antibiotics studied (trimethoprim, nor-

floxacin, ciprofloxacin, ofloxacin, clindamycin, metronida-

zole) showed variation in concentration throughout the day

Fig. 2 e Inter-day variation in cocaine (COC) and MDMA consum

wastewater of 19 cities e adapted from Thomas et al. (2012).

(Coutu et al., 2013). A peak in receiving load was observed

between 07:00 and 9:00 h (Fig. 1a). This is explainable by

posology and the accumulation of administered drugs within

urine during sleep (Pl�osz et al., 2010; Coutu et al., 2013).

Therefore following the first toilet flush of the day an increase

in load of antibiotics is observed. Other urine derived ECs are

expected to behave in the same way. Yet it is unknown how

the performance of WwTWs responds to this daily spike in

receiving concentration and volumetric load. Collection of

corresponding final effluent samples would help address this.

3.1.4. Inter-day variability A European wide study of 19 European countries using a va-

riety of time, flow and volume proportional sampling

attempted to investigate inter-day variability (i.e., over a one

week period) of illicit drugs in influent wastewater (Thomas

et al., 2012). Collated data revealed a trend of recreational

use for some compounds (Fig. 2). Both benzoylecgonine (the

major metabolite of cocaine) and MDMA showed elevated

levels at weekends. In contrast, it is anticipated that hospital

dispensed chemicals such as X-ray contrast media and anti-

cancer drugs will be observed at higher concentrations

ption calculated from a European wide study profiling

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 7 11

during weekdays when hospital appointments are greatest.

Inter-day variability of EC concentration is difficult to fully

appreciate without conducting flow proportional sampling.

This is most significant when comparing weekday and

weekend periods where wastewater flows can vary notably.

Differences in weekday/weekend flow will be specific for the

catchment in question. For example, a residential (commuter)

or tourist area will see increased flow during weekends. On

the other hand, a catchment containing industrial works may

observe a reduction in flow and dilution during weekends.

Rainfall will also result in changes to wastewater flow be-

tween days. This illustrates that reporting load (mass per day)

over the traditional approach of concentration (mass per litre)

to overcome temporal variations in flow is more suited to

describe findings.

3.1.5. Seasonality Some ECs have seasonal uses indicating that their influent

load will vary throughout the year. For example, monthly

prescription information for the UK showed antihistamines

used to treat allergies (e.g., hay fever) peaked from May to

August when pollen production is greatest (Fig. 3) (National

Health Service, 2012). Generally, quantities prescribed were

100% greater in summer compared to winter months. This

relationship has been established for the antihistamine

cetirizine in Norwegian wastewater (Harman et al., 2011).

Fig. 3 e Monthly National Health Service Prescription Cost Ana

appliance contractors in England) for antihistamines (loratadine

(a), sunscreen and sunscreening preparations (b), pseudoephed

Health Service, 2012).

Prescription information also showed a similar trend for the

number of prescribed sunscreens and sunscreening prepara-

tions (Fig. 3). This indicates that personal care products

associated with these preparations such as UV filters will also

be more prevalent in wastewater during summer. In contrast,

pseudoephedrine (used in nasal decongestants) and pholco-

dine (used in cough preparations) showed an opposite trend in

their usage with highest prescription observed during winter

months (Fig. 1) (National Health Service, 2012). Cumulative

concentration of ephedrines has been reported at higher

concentrations during winter months (Kasprzyk-Hordern and

Baker, 2012a). Furthermore, a high contribution of the enan-

tiomer 1S,2S(þ)-pseudoephedrine during winter is consistent with increased usage of over-the-counter-medication used to

treat symptoms of cold (Kasprzyk-Hordern and Baker, 2012b).

A study by Veach and Bernot (2011) attempted to determine

temporal variations of 15 ECs within two rivers over a one year

period. However, the sampling protocol consisted of a single

monthly grab sample which severely restricts understanding

of seasonal variation. A more robust approach which under-

took weekly 24 h flow proportional sampling every month for

a one year period, investigated temporal variations of antibi-

otics within influent wastewater (Coutu et al., 2013). Season-

ality was reported for both ciprofloxacin and norfloxacin in

receiving wastewater for a WwTW in Switzerland (Fig. 1b).

Mass fluxes were higher in winter and spring months,

lysis information (items dispensed by pharmacy and

, cetirizine hydrochloride and fexofenadine hydrochloride)

rine hydrochloride (c) and pholcodine (d) in 2012 (National

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 712

explained by seasonal therapeutic use. For example, cipro-

floxacin is used to treat airway infections and infections

affecting the throat, nose and ear (Coutu et al., 2013). This

relationship could not be established for UK Prescription Cost

Analysis data (National Health Service, 2012).

3.1.6. Occasional events There is a lack of knowledge on the impact of occasional

events to the loading of ECs in wastewater, or their impact to

wastewater treatment performance and to biota of the

receiving environment. An increase in drug use may be

anticipated during music festivals (Lai et al., 2013a), public

holidays (Lai et al., 2013b), major sporting events (Gerrity et al.,

2011) and by students during exam periods for improved

concentration (Dennhardt and Murphy, 2013). A less predic-

tive source of increased drug load can be observed during the

direct disposal of a large quantity of illicit drug during a police

raid of an illegal production factory. To demonstrate, Emke

et al. (2014) observed a load of MDMA 20 times higher than

normal during a weekly sampling campaign at a WwTW in

Utrecht. Direct disposal was considered to be the source of

this spike based on findings of enantiomeric profiling (i.e., no

metabolism) (Fig. 4). This irregularly high load coincided with

a police raid within the catchment two days earlier. However,

Fig. 4 e Load of MDMA in wastewater collected during

week long sampling campaigns conducted in 2010 and

2011 (a) and their corresponding enantiomeric fraction (b)

e adapted from Emke et al. (2014). Note: Enantiomeric

fraction ¼ (þ)/[(þ)þ(¡)] e 0.5 denotes a racemic distribution.

such events are difficult to monitor due to their unpredictable

occurrence. A further scenario which could result in a sharp

increased load of a drug within the environment is in the

event of a pandemic. A UK outbreak of influenza in 2009

resulted in a high number of people treated with antivirals

and antibiotics (Singer et al., 2011; Slater et al., 2011; Singer

et al., 2013). There is limited knowledge of the resilience of

WwTWs to maintain normal performance and achieve anti-

biotic removal in such events. Nevertheless, laboratory scale

investigation showed that typically reported concentrations

of oseltamivir carboxylate (the active metabolite of Tamiflu®,

the medicine used in response to the influenza outbreak)

reduced activated sludge performance with respect to

nutrient removal (Slater et al., 2011).

3.2. Partitioning of ECs to solid matter during wastewater treatment

3.2.1. Influent wastewater and final effluent Emerging contaminant analysis in wastewaters tends to be

undertaken on the aqueous phase only (i.e., a pre-filtered

sample) (Table 1). Particulate phase analysis is not routinely

undertaken because up to one gram of dry solids is often

required for each analysis and additional sample extraction is

needed (Radjenovi�c et al., 2009a; Baker and Kasprzyk-Hordern,

2011b). However, analysis here is essential as some chemicals

have a high affinity to particulate matter. Numerous chem-

icals including amitriptyline, EMDP, dosulepin, fluoxetine,

norfluoxetine, triclosan, ofloxacin and ciprofloxacin have

been found to be within the particulate phase of influent

wastewater at significant concentrations (>20% of the total concentration) (Baker and Kasprzyk-Hordern, 2011a,b; Petrie

et al., 2014c). Consequently, particulate phase determination

is necessary to correctly report influent concentration for

some chemicals. This is also essential for back calculations of

population usage by wastewater-based epidemiology (Baker

et al., 2012). Partitioning of ECs to suspended matter in final

effluents is even less studied due to the very low solids con-

centrations encountered (typically 10e30 mg l�1 for secondary processes). Nevertheless, Petrie et al. (2014c) found that final

effluent of secondary processes had >20% of the total triclo- san, ofloxacin and ciprofloxacin concentration to be within

the particulate phase, despite very low suspended solids

concentrations. Particulate phase concentrations were

equivalent to, and in the range 26e296 ng l�1, respectively. This provides a route for their release into the environment

which goes unmonitored and the environmental fate of these

particulate bound chemicals is unknown.

3.2.2. Diagnosis of EC removal during wastewater treatment Common sampling approaches to measure wastewater

treatment performance with respect to EC removal monitor

aqueous phase of influent and effluent samples only (Table 1).

To better understand their pathways of removal during

wastewater treatment, particulate phase analysis is needed as

well as analysis of the biomass (either suspended or attached)

of the process (Petrie et al., 2014a,c). Ideally, corresponding

aqueous and particulate determinations should be under-

taken for each sampling point such that a complete process

mass balance is attained. This can give valuable information

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 7 13

on the dominant mechanisms which govern removal (Petrie

et al., 2014a,c). Removal can vary greatly between ECs from a

physically driven process (adsorption) to biologically medi-

ated enzymatic reactions (biodegradation) (Helbling et al.,

2010; Petrie et al., 2014a). Their identification also needs to

be supported with information of process conditions and

operation, nutrient removal and complimentary analysis of

the physical/biological characterisation of biomass (Petrie

et al., 2014a). With this knowledge, the operation of the pro-

cess could be adjusted to favour their removal (Petrie et al.,

2014b). Such information can also be used to identify where

further research efforts may be needed. For example, those

chemicals removed by biodegradation suggest further inves-

tigation of possible biotransformation products in final efflu-

ents is needed, and those removed by adsorption require

further understanding of their fate during and following

sludge treatment.

3.2.3. Biosolids and amended soils During anaerobic digestion, biosolids (or treated sludge) are

generated. These are often applied to agricultural land as a

fertiliser in some countries. Despite lengthy digestion (20e30

days) and outdoor storage for up to six months following

treatment, some ECs have shown to persist (Cort�es et al.,

2013). No legislation currently controls the use of biosolids

on agricultural land with respect to concentration of ECs.

Consequently, a lack of analysis has been observed here

(Table 2). The majority of chemicals previously investigated in

biosolids were found at <1 mg kg�1. On the other hand bisphenol A, triclosan, triclocarban and the antibiotics cipro-

floxacin, ofloxacin and norfloxacin have all been reported at

>1 mg kg�1 (Table 2) (Golet et al., 2002; McAvoy et al., 2002; Lindberg et al., 2005; Morales et al., 2005; Heidler et al., 2006;

Chu and Metcalfe, 2007; Ying and Kookana, 2007; Stasinakis

et al., 2008; Cha and Cupples, 2009; Langdon et al., 2011;

Gottschall et al., 2012; Sabourin et al., 2012; Guerra et al.,

2014). These chemicals have very different physicochemical

properties (hydrophobicity, water solubility etc) (Table 2),

suggesting their fate and transport in amended soils will vary

(Morais et al., 2013). Triclosan and triclocarban exhibit greater

hydrophobicity (log Kow 4.2e4.8) indicative of retention within

the soil matrix. In contrast, those which are relatively water

soluble (�3.0 � 104 mg l�1 e ciprofloxacin, norfloxacin and ofloxacin) suggest hydrophilic mobility which may result in

their transport to surrounding surface waters. However, Tolls

(2001) demonstrated that antibiotics show a wide range of

mobility in soil. This implies other mechanisms are likely to be

important, particularly for charged ECs. Partitioning behav-

iour of charged ECs is likely to be governed by other mecha-

nisms such as electrostatic attractions (Hyland et al., 2012). If

charge interactions play a role in sorption, then the Kow concept is not meaningful here and cannot be applied.

Therefore the acid-dissociation constant (pKa) of the EC in

question and pH of the matrix are critical for better under-

standing partitioning behaviour. For those ECs which are

ionisable, the pH dependant octanolewater coefficient (log

Dow) should be applied to account for different behaviour.

To better appreciate the fate of ECs in soils, long term field

studies at real environmental conditions are needed. These

require sampling of biosolids, soil (pre- and post-application,

and at various depths), surrounding surface waters and up-

take by plants over an extended time period. Supporting in-

formation of pH, rainfall, temperature, sunlight and soil type/

characteristics would help understand their fate. An excellent

study by Butler et al. (2012) found that following biosolids

application, triclosan (~0.8e1.0 mg kg�1) showed little change in concentration over the initial eight months for three

different soil types. However, after 12 months less than 20% of

the initial concentration was recovered. A large fraction of this

was attributed to the biological transformation of triclosan to

methyl triclosan. Methyl triclosan was observed up to

0.4 mg kg�1, demonstrating that transformation products

need to be investigated here.

3.3. Fate of ECs in environmental waters

3.3.1. Human metabolites Parent chemicals are often excreted from the human body

with a number of associated metabolites. As an example,

ibuprofen is excreted as the unchanged drug (1%) and several

metabolites: (þ)-2-40-(2-Hydroxy-2-methylpropyl)-phenyl- propionic acid (25%), (þ)-2-40-(2-carboxypropyl)-phenyl- propionic acid (37%) and conjugated ibuprofen (14%)

(Kasprzyk-Hordern et al., 2008b). However, only ~20% of ECs

previously reported in UK waters were metabolites (Table 1),

and this is mirrored in international studies (Gros et al., 2012;

Hughes et al., 2013; L�opez-Serna et al., 2013). Admittedly their

determination has been restricted by a lack of available

analytical reference standards. Despite this, their determina-

tion is essential as free parent ECs are often cleaved from

human metabolites and in particular, glucuronide conjugates

when exposed to environmental conditions (Ternes et al.,

1999). This has been observed in crude sewage and activated

sludge batch studies for steroid estrogens (17a-EE2 3-

glucuronide, estriol 16a-glucuronide, and estrone 3-

glucuronide) (Gomes et al., 2009) and suggested to occur spe-

cifically for carbamazepine during full-scale activated sludge

treatment (Vieno et al., 2007). Furthermore, analysis of me-

tabolites is necessary as they can be found at concentrations

much greater than the corresponding parent chemical and

can themselves be pharmacologically active (Kasprzyk-

Hordern et al., 2008b). To demonstrate, a major metabolite of

carbamazepine (carbamazepine epoxide) has been found in

influent wastewater at concentrations ranging from 880 to

4026 ng l�1 whereas the parent compound was found at <1.5e113 ng l�1 (Huerta-Fontela et al., 2010). Metabolites can also be persistent during secondary wastewater treatment

(Lajeunesse et al., 2012; Petrie et al., 2013b). Their release into

the environment and the possibility of subsequent biotrans-

formation/deconjugation in environmental compartments to

the parent EC makes their determination essential to better

assess ecological risk.

3.3.2. Microbial transformation Many ECs undergo microbially mediated reactions during

secondary wastewater treatment (Helbling et al., 2010), and in

the environment. Biodegradation is often referred to as the

dominant fate pathway for the removal of some ECs from the

aqueous phase of wastewaters and surface waters (Andersen

et al., 2005; Bagnall et al., 2012a,b). However, this can result in

Table 2 e Concentration and physicochemical properties (ChemicalBook, 2014; ChemSpider, 2014; DrugBank, 2014) of ECs found in biosolids (n ≥ 3).

Emerging contaminant

Log Kow pKa Water solubility (mg l�1)

Country Mean biosolid conc. (mg kg�1 dry weight)

Reference

Estrone 3.13 10.3 30.0 Germany <MQL-0.02 Ternes et al., 2002 Canada 0.06 Sabourin et al., 2012

Australia <MQL-0.28 Langdon et al., 2011 Ibuprofen 3.97 4.91 21.0 Spain 0.30 Radjenovi�c et al., 2009a

Canada 0.15a Guerra et al., 2014

Spain 0.02e0.44 Albero et al., 2014

Canada 0.17 Sabourin et al., 2012

Canada 0.06 Gottschall et al., 2012

Spain 0.09 Morais et al., 2013

Naproxen 3.18 4.15 15.9 Canada 0.02a Guerra et al., 2014

Spain <MQL-0.02 Albero et al., 2014 Canada 0.01 Sabourin et al., 2012

Diclofenac 4.51 4.15 2.37 Spain 0.19 Radjenovi�c et al., 2009a

Spain <MQL-0.63 Albero et al., 2014 Spain 0.06 Morais et al., 2013

Canada <MQL-0.02 Spongberg and Witter, 2008 Acetaminophen 0.460 9.38 1.40 � 104 Spain 0.03 Radjenovi�c et al., 2009a

USA <MQL-0.37 Ding et al., 2011 Canada 0.02a Guerra et al., 2014

Spain 0.02e0.29 Albero et al., 2014

Gemfibrozil 3.40 4.75 e Spain 0.12 Radjenovi�c et al., 2009a

Spain <MQL-0.07 Albero et al., 2014 Canada 0.008 Sabourin et al., 2012

Canada <MQL-0.003 Spongberg and Witter, 2008 Carbamazepine 2.45 13.9 17.7 Spain 0.08 Radjenovi�c et al., 2009a

USA <MQL-0.02 Ding et al., 2011 Canada 0.26 Miao et al., 2005

Canada 0.18 Gottschall et al., 2012

Canada 0.09 Sabourin et al., 2012

Spain 0.03 Morais et al., 2013

Canada 0.01 Spongberg and Witter, 2008

Fluoxetine 4.05 3.95 5.00 � 104 Spain 0.12 Radjenovi�c et al., 2009a,b Canada 0.09 Sabourin et al., 2012

Canada 0.11 Gottschall et al., 2012

Propranolol 3.48 9.42 61.7 Spain 0.03 Radjenovi�c et al., 2009a

Canada 0.04 Sabourin et al., 2012

Canada 0.12 Gottschall et al., 2012

Canada 0.36a Guerra et al., 2014

Canada 0.23 Gottschall et al., 2012

Ciprofloxacin 0.280 6.09 3.00 � 104 Switzerland 2.27e2.42 Golet et al., 2002 Sweden 0.50e4.80 Lindberg et al., 2005

Canada 6.50a Guerra et al., 2014

Canada 5.87 Sabourin et al., 2012

Canada 3.26 Gottschall et al., 2012

Canada <MQL-0.05 Spongberg and Witter, 2008 Norfloxacin �1.03 2.75 1.78 � 105 Switzerland 2.13e2.37 Golet et al., 2002

Sweden 0.10e4.20 Lindberg et al., 2005

Canada 1.75 Sabourin et al., 2012

Canada 1.01 Gottschall et al., 2012

Ofloxacin �0.390 5.23 2.83 � 104 Sweden <MQL-2.00 Lindberg et al., 2005 Spain 0.07 Radjenovi�c et al., 2009a,b

Canada 0.69a Guerra et al., 2014

Canada 1.07 Sabourin et al., 2012

Canada 1.40 Gottschall et al., 2012

Tetracycline �1.30 3.30 5.00 � 104 USA <MQL-0.56 Ding et al., 2011 Canada 0.24a Guerra et al., 2014

Canada 0.34 Sabourin et al., 2012

Canada 0.51 Gottschall et al., 2012

Canada <MQL-0.01 Spongberg and Witter, 2008

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 714

Table 2 e (continued)

Emerging contaminant

Log Kow pKa Water solubility (mg l�1)

Country Mean biosolid conc. (mg kg�1 dry weight)

Reference

Triclosan 4.80 7.90 10.0 Greece 3.21 Stasinakis et al., 2008

USA 0.53e15.6 McAvoy et al., 2002

Spain 1.51 Morales et al., 2005

Canada 0.68e11.55 Chu and Metcalfe, 2007

Australia 5.58 Ying and Kookana, 2007

USA 4.89e9.28 Cha and Cupples, 2009

Canada 6.80a Guerra et al., 2014

Canada 4.68 Sabourin et al., 2012

Canada 10.9 Gottschall et al., 2012

Australia 0.22e9.89 Langdon et al., 2011

Triclocarban 4.20 12.7 0.110 Canada 3.05e5.97 Chu and Metcalfe, 2007

USA 0.09e7.06 Cha and Cupples, 2009

USA 51.0 Heidler et al., 2006

Canada 2.90a Guerra et al., 2014

Canada 6.03 Sabourin et al., 2012

Canada 4.94 Gottschall et al., 2012

Bisphenol A 3.43 10.3 120 Greece 0.53 Stasinakis et al., 2008

China 0.01 Song et al., 2014

Australia 0.06e1.37 Langdon et al., 2011

Key: Log Kow, octanolewater coefficient; pKa, acid dissociation co-efficient; MQL, method quantitation limit. a Reported as median.

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 7 15

the formation of numerous degradation or transformation

products. These are not well studied as typical targeted

screening approaches for known compounds (low resolution

mass spectrometry utilising triple quadrupoles technology)

are not capable of their identification. Also, as biotransfor-

mation pathways are not often known, there are very few

standards available for these transformation products.

Helbling et al. (2010) successfully identified previously unre-

ported biological transformation products for the pharma-

ceuticals bezafibrate, diazepam (Fig. 5a), levetiracetam,

oseltamivir and valsartan using high resolution mass spec-

trometry (linear ion trap-orbitrap technology) during

(a)

Fig. 5 e Transformation pathways of diazepam during activated

and when exposed to simulated sunlight (b) e adapted from W

laboratory scale investigation of activated sludge. Trans-

formation products have also been reported at indigenous

concentrations in final effluents of activated sludge WwTWs

(G�omez et al., 2010). G�omez et al. (2010) identified trans-

formation products of acetaminophen (P-aminophenol) and

azithromycin (unnamed compound) by non-targeted

screening using quadrupole time of flight (QTOF) mass spec-

trometry. Their identification is essential as these can be more

toxic than the parent compound as is the case with P-ami-

nophenol (Bloomfield, 2002; G�omez et al., 2010). Therefore

removal of the parent EC does not necessarily translate into

removal of toxicity. Considering the high number of parent

(b)

sludge treatment (a) e adapted from Helbling et al. (2010)

est and Rowland (2012).

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 716

ECs in wastewater (Table 1) (Hughes et al., 2013), it is expected

that a great number of transformation products (of unknown

toxicity and persistence) exist in final effluent and receiving

surface waters.

3.3.3. Physicochemical processes Physicochemical mechanisms can also contribute to EC

removal from wastewaters and surface waters. Sorption onto

biomass during wastewater treatment, or into sediments

when present in the riverine environment will result in

removal from the aqueous phase. However, this is only likely

to hold true for some ECs. For example, if equilibrium is

established between the biomass or sediment and the

aqueous medium for a specific EC, the net exchange between

the two phases (and removal from the aqueous phase) will be

zero (Schwarzenbach et al., 2003). Therefore sorption will not

contribute to their removal. This has been observed across

activated sludge WwTWs for some ECs such as steroid estro-

gens (Andersen et al., 2005; Petrie et al., 2014a). On the other

hand, the antibiotics ofloxacin and ciprofloxacin were shown

to be removed by sorption during wastewater treatment due

to their high affinity to solid organic matter (Petrie et al.,

2014c). Therefore understanding the role of physicochemical

properties to the sorption of these ECs is essential. Consider-

ation must also be given to the impact of dissolved organic

matter to the fate of ECs in the environment. Binding of ECs to

dissolved organic matter could help retain ECs in the aqueous

phase of environmental matrices. Furthermore, formation of

EC-dissolved organic matter complexes may lead to the EC

going undetected during analysis.

When present in the aqueous environment, ECs are sus-

ceptible to breakdown by photolysis. Photolysis has been

shown to successfully degrade several ECs such as ketoprofen,

propranolol, naproxen, E2, EE2, gemfibrozil and ibuprofen in

river water (Lin and Reinhard, 2005). Half-lives ranged broadly

from four minutes for ketoprofen to 15 h for gemfibrozil and

ibuprofen. This range of susceptibility to breakdown by

photolysis observed is attributed to differences in their

chemical structure. For example, the carbonyl moiety of

ketoprofen is in conjugation with two aromatic rings which

results in a very reactive triple state and a high susceptibility

to breakdown by photolysis (Lin and Reinhard, 2005). There-

fore photolysis can contribute notably to the removal of a

number of ECs from surface waters. As with biological

degradation, removal of the parent compound by photolysis is

not indicative of complete mineralisation and several trans-

formation products can be observed (Fig. 5b). Therefore a

reduction in toxicity may not be observed with removal of the

parent compound. It can be postulated that the presence of

dissolved organic matter of comparatively high concentra-

tion, as well as particulates in environmental waters will

reduce EC degradation kinetics by clouding sunlight intensity.

However, West and Rowland (2012) found that humic acid (a

small molecular weight charged species) slowed or increased

degradation rate, dependant on the specific EC investigated.

Increased degradation in the presence of humic acid or ni-

trates can be attributed to indirect photolysis (Andreozzi et al.,

2003). Wastewater effluents contain hydroxyl radicals and

triplet excited state organic matter which facilitates indirect

photolysis of some ECs (Ryan et al., 2011). Other

environmental factors such as depth of river, shading from

bankside vegetation, presence of particulate matter and sea-

son also require further investigation to assess their impact to

EC photolysis in environmental conditions.

3.4. Toxicological impact of ECs within the environment

3.4.1. Collated acute toxicity information Testing aquatic ecotoxicity of ECs is usually undertaken at

controlled laboratory conditions. This often involves deter-

mining acute toxicity of a single compound to a specific in-

dicator species. The most common taxon is the crustacean

Daphnia magna, with standard methods available to measure

EC50 based on their mobility (OECD, 2004). These methods are

conducted in an exposure medium consisting of clean labo-

ratory water. Such tests are an excellent indicator of the po-

tential risks posed by individual chemicals. The general

approach is to classify those with an EC50 between 10 and

100 mg l�1 as harmful, from 1 to 10 mg l�1 as toxic, and those <1 mg l�1 as very toxic to aquatic organisms (Commission of the European Communities, 1996; Cleuvers, 2003). Collation of

EC50data from different literature sources has limitations due

to the range of test species used, as well as the variety of

toxicological endpoints studied. Effect concentrations will be

dependent on the test species studied as ECs can cause

different toxicological responses between different species.

Despite this, collated data can give an indication of toxicity for

the most well studied ECs (Fig. 6). Non-steroidal anti-inflam-

matory drugs (acetaminophen, ibuprofen, diclofenac and

naproxen), lipid regulators (bezafibrate, clofibric acid e

metabolite), carbamazepine and trimethoprim are generally

classified as harmful to aquatic organisms. Other antibiotics

(ofloxacin, sulfamethoxazole, oxytetracycline, erythromycin)

mostly fall within the toxic range. However, there is also a lack

of information on the synergistic impact of these chemicals,

particularly at low concentration over longer exposure times.

3.4.2. Impact of mixtures and chronic impact Aquatic biota within the receiving environment is continually

exposed to a complex mixture of ECs. Studies have shown that

mixtures of pharmaceuticals exhibit greater effect than the

compounds individually. To demonstrate, the antiepileptic

carbamazepine and the lipid lowering agent clofibric acid

(which belong to very different therapeutic classes and

expectant modes of action), exhibited stronger effects to D.

magna during immobilization tests than the single com-

pounds at the same concentration (Cleuvers, 2003). Further-

more, Cleuvers (2004) reported considerable acute toxicity for

a mixture of NSAIDs (diclofenac, ibuprofen, naproxen and

aspirin) at the same concentration where little or no effect

was observed for the chemicals individually. This underpins

the need to assess chronic impact of EC mixtures at environ-

mentally relevant concentrations, as well as undertaking

whole life cycle determinations. However, this is very difficult

to ascertain over long time periods where relatively subtle

toxicological responses are observed in comparison to acute

effects such as death or mobility which are easily measurable.

Nevertheless, increasing numbers of studies assessing the

chronic impact of EC mixtures have found significant effects.

For example, female Danio rerio exposed to a pharmaceutical

Fig. 6 e Ecotoxicity EC50 information (n ≥ 5) available in the literature for ECs reported for a minimum of three different species types with boxes showing interquartile range and median, and whiskers showing range, outliers also shown. It

should be noted that collated toxicological response information only gives a subjective insight into toxicity as this is highly

dependent on the test species studied as ECs can cause varying toxicological responses between species type. Data obtained

from: (Henschel et al., 1997; Holten Lützhøft et al., 1999; Wollenberger et al., 2000; Cleuvers, 2003; Ferrari et al., 2003; Pro

et al., 2003; Cleuvers, 2004; Eguchi et al., 2004; Pomati et al., 2004; Cleuvers, 2005; Isidori et al., 2005a,b; Heckmann et al.,

2007; Isidori et al., 2007; Kim et al., 2007; DeLorenzo and Fleming, 2008; Park and Choi, 2008; Quinn et al., 2008b; De Liguoro

et al., 2009; Rosal et al., 2010; Han et al., 2010; Van den Brandhof and Montforts, 2010; Dave and Herger, 2012). EC50's classified as <1 mg l¡1 ¼ very toxic to aquatic organisms, 1e10 mg l¡1 ¼ toxic to aquatic organisms, 10e100 mg l¡1 ¼ harmful to aquatic organisms and >100 mg l¡1 ¼ not classified (Commission of the European Communities, 1996; Cleuvers, 2003).

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 7 17

mixture (acetaminophen, carbamazepine, gemfibrozil and

venlafaxine) as well as WwTW effluent showed significant

reductions in embryo production over a six week period (Galus

et al., 2013). Also, Dietrich et al. (2010) investigated single

compound and mixture toxicity of carbamazepine (500 ng l�1), diclofenac (360 ng l�1), EE2 (0.1 ng l�1) and metoprolol (1200 ng l�1) to D. magna over six generations. These concen- trations were selected to represent environmentally relevant

concentrations for river waters in Germany (Dietrich et al.,

2010). However, the influence of the pharmaceutical mixture

was inconsistent and unpredictable. This is in disagreement

to acute toxicity testing of pharmaceutical mixtures con-

ducted at greater concentrations (Cleuvers, 2003, 2004). Also,

no relationship in toxicological effect was observed for suc-

cessive generations of D. magna. It was postulated that this

may be due to the development of resistance towards the

pharmaceuticals (Dietrich et al., 2010). It is clear that further

studies are needed to confirm the chronic impact of multiple

ECs synergistically, whilst at low concentration. Furthermore,

these studies need to assess ecological impact to organisms of

different trophic levels. Consideration must also be given to

the exposure medium used. Surface waters contain dissolved

organic matter which could potentially reduce compound

bioavailability. This is not currently considered in existing

laboratory toxicity testing protocols.

3.4.3. Illicit drugs Despite numerous illicit drugs being reported in surface wa-

ters (Table 1) and the possibility of shock loads which can be

encountered (Emke et al., 2014), there are only a few experi-

mental studies which have investigated toxicity to aquatic

organisms. Illicit drugs are potent in nature therefore a high

toxicity to exposed aquatic organisms is inferred. Parolini and

Binelli (2014) investigated oxidative and genetic responses

induced by D-9-tetrahydrocannabinol (main psychoactive

compound of cannabis) to the mollusc Dreissena polymorpha

Table 3 e Toxicological response of chiral enantiomers and their enantiomeric distribution in the environment.

Emerging contaminant

Enantiomer Toxicological response of chiral enantiomers Distribution of chiral enantiomers within the environment

Potency within the human body

Toxicity to aquatic organisms

Country Surface water Enantiomeric fraction

Reference

Ibuprofen S(�); R(þ) S > 100 times more potenta e Switzerland Lakes and rivers 0.41e0.47 Buser et al., 1999 Spain Rivers 0.30e0.50 L�opez-Serna et al., 2013

Australia River 0.60e0.80 Khan et al., 2013

China Canal and river 0.13e0.33 Wang et al., 2013

Naproxen S(�); R(�) S more potentk e Australia River 1.00 Khan et al., 2013 Propranolol S(�); R(þ) S 100 times more activea Similar acute response to

P. promelas and D. magnac USA Rivers 0.21e0.53 Fono and Sedlak, 2005

S higher chronic toxicity

to P. promelasc UK River 0.45 Bagnall et al., 2012a,b

S and R chronic toxicity

similar to D. magnac Spain Rivers 0.38e0.60 L�opez-Serna et al., 2013

Atenolol S(�); R(þ) S more potentf S ~4 times more toxic to T. thermophilad

UK River 0.38e0.56 Kasprzyk-Hordern

and Baker, 2012b

R ~2 times more toxic to

D. magnad UK River 0.47 Bagnall et al., 2012a,b

S and R toxic effects similar

to P. subcapitad Spain Rivers 0.38e0.50 L�opez-Serna et al., 2013

Sotalol R/S(±) R possesses majority of b-blocking activityh

e Spain Rivers 0.41e0.65 L�opez-Serna et al., 2013

Metoprolol R/S(±) S 35 times more potentg e Spain Rivers 0.42e0.51 L�opez-Serna et al., 2013 Germany River 0.43e0.46 Kunkel and Radke et al., 2012

Fluoxetine S(þ); R(�) S and R have different pharmacological activitye,i

S ~10 times more toxic to

P. promelasb Spain Rivers 0.64e0.68 L�opez-Serna et al., 2013

S and R toxic effects similar

to D. magnab,d

S ~10 times more toxic to

T. thermophilad

Norfluoxetine R/S(±) Enantiomers have different potencyi

e Spain Rivers 0.76 L�opez-Serna et al., 2013

Venlafaxine R/S(±) R more potentj e UK River 0.40e0.65 Kasprzyk-Hordern and Baker, 2012b

UK River 0.58 Bagnall et al., 2012a,b

France River 0.46e0.74 Li et al., 2013

Ephedrine 1R,2S(�); 1S,2R(þ) e e UK River 0.79e1.00 Kasprzyk-Hordern and Baker, 2012b

MDMA S(þ); R(�) S more potentl e UK River 0.56e0.81 Kasprzyk-Hordern and Baker, 2012b

MDA R/S(±) S more potentl e UK River 0.56e0.58 Kasprzyk-Hordern and Baker, 2012b

Amphetamine S(þ); R(�) Dextro enantiomer more potento

e UK River 0.81e0.86 Kasprzyk-Hordern and

Baker, 2012b

w a t e r

r e s e a r c h

7 2

( 2 0 1 5 ) 3 e 2 7

1 8

N o te : E n a n ti o m er ic

fr a ct io n ¼

(þ )/ [( þ)

þ( �)

] e

0 .5

d e n o te s a ra c e m ic

d is tr ib u ti o n .

a K a s p rz y k -H

o rd

e rn

, 2 0 1 0 .

b S ta n le y e t a l. , 2 0 0 7 .

c S ta n le y e t a l. , 2 0 0 6 .

d D e A n d r� e s e t a l. , 2 0 0 9 .

e S te in e r e t a l. , 1 9 9 8 .

f P e a rs o n e t a l. , 1 9 8 9 .

g S p a h n e t a l. , 1 9 8 9 .

h M e h v a r a n d B ro

c k s , 2 0 0 1 .

i S te v e n s a n d W

ri g h to n , 1 9 9 3 .

j L e re r,

2 0 0 2 .

k H o n jo

e t a l. , 2 0 1 1 .

l N a ti o n a l H ig h w a y T ra ffi c S a fe ty

A d m in is tr a ti o n , 2 0 1 4 .

o H e a l e t a l. , 2 0 1 3 .

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 7 19

(zebra mussel). Concentrations of 500 ng l�1 over a 14 day

exposure period led to notable changes in oxidative status.

This could lead to increased lipid peroxidation, protein

carbonylation and DNA damage (Parolini and Binelli, 2014).

The possible effects of cocaine and its metabolites to aquatic

organisms have been given more attention (Binelli et al., 2012;

Parolini et al., 2013; Parolini and Binelli, 2013). An initial study

investigated cyto-genetoxic effects of cocaine to D. polymorpha

at concentrations of 40, 220 and 10,000 ng l�1 (Binelli et al., 2012). Primary DNA damage, increased micro-nucleated cells

and apoptosis were all observed after an exposure time of

96 h, even at the lowest exposure concentration (40 ng l�1). An

investigation of sub-lethal effects of benzoylecgonine showed

enzyme defence chain imbalances of D. polymorpha at

500 ng l�1 for a 14 day time period (Parolini et al., 2013). Finally, a study of ecognine methyl ester (another cocaine metabolite)

found 14 days exposure to 500 ng l�1 induced destabilization of lysosome membranes, inactivation of defence enzymes,

increased lipid peroxidation, protein carbonylation and DNA

fragmentation of D. polymorpha (Parolini and Binelli, 2013).

These studies have given an excellent insight into the toxicity

of illicit drugs to aquatic organisms. Toxicity testing of illicit

drugs should be expanded to include compounds such as

MDMA, which have not been investigated previously. Addi-

tionally, these findings need to be supported with standard

toxicity testing protocols for a variety of accepted indicator

species types. Coupled with environmental occurrence infor-

mation, this would help prioritise those chemicals which

require immediate in-depth investigation of their fate and

ecological impact.

3.4.4. River sediments and amended soils Emerging contaminants have also been reported in riverine

sediments (Da Silva et al., 2011; Azzouz and Ballesteros, 2012;

Chen and Zhou, 2014). Determining toxicological impact here

is essential as sediments can act as a sink for their accumu-

lation. However, this is notoriously difficult to ascertain due to

the complexity of the system. Benthic organisms can be

exposed to ECs within the sediment itself, in interstitial water

and in overlying water (Gilroy et al., 2012). This makes

experimental design and set-up critical for reproducing

representative conditions. A study investigating toxicity of

diclofenac to the crustacean Hyalella azteca, prepared syn-

thetic sediment at a 1:3 ratio with water (Oviedo-G�omez et al.,

2010). The authors failed to report or investigate aqueous/

liquid partitioning of diclofenac, whether equilibrium condi-

tions were present or if desorption occurred throughout the

test. Consequently, reporting toxicological effect concentra-

tions for sediments can have considerable uncertainties. An

improved study by Gilroy et al. (2012) supported biological

response information with chemical analysis for both

aqueous and particulate phases. Findings suggested that

sorption to sediments resulted in a reduction of bioavailability

and toxicity. On the other hand, the accumulation (and

increased concentration) of readily sorbed compounds in

sediments within the environment could compensate for this

reduction in toxicity. Interestingly, the activity of benthic

invertebrate also resulted in increased desorption, leading to

improved bioavailability (Gilroy et al., 2012). Numerous

mechanisms take place, illustrating the complexity of

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 720

ascertaining sediment toxicity. Studies over long time periods

which simulate representative steady-state riverine sediment

conditions are required for a range of indicator species. These

firstly need to be supported with chemical analysis to assess

the behaviour of the EC(s) in question. Such studies would

help assess chronic and multi-generation impact of sediment

contamination to benthic organisms. Similarly (and as dis-

cussed previously e Section 3.2.3), amended soils can also

contain significant concentrations of some municipal derived

ECs. The resulting toxicity to terrestrial organisms is poorly

understood due to the lack of knowledge on their occurrence

to drive investigation of their toxicological impact. Also, no

standard methods are available which can easily measure

toxicity to exposed terrestrial organisms. Nevertheless, indi-

rect methods such as soil respiration rate can be used to

measure possible impact to the soil community (Butler et al.,

2011). These need to be supported with controlled single

(and multi) compound toxicity testing with predetermined

indicator species to help understand the ecological impact of

applying biosolids to agricultural soils.

3.4.5. Chirality Pharmaceuticals are often prepared and dispensed as racemic

mixtures (i.e., a 50:50 mixture of two enantiomers). Meta-

bolism within the human body and exposure to biological

mediated reactions during wastewater treatment can result in

the enrichment of one specific enantiomer. On the other hand,

some pharmaceuticals such as NSAIDs are marketed as a

single enantiomer. Chiral inversion can lead to the distribu-

tion of two enantiomers. This has been observed for naproxen

during biological wastewater treatment (Hashim et al., 2011).

However, conventional analytical approaches are not able to

distinguish between enantiomers of the same compound

(Evans and Kasprzyk-Hordern, 2014). As a result there is a

paucity of information on the enantiomeric distribution of

chiral compounds within the environment. Nevertheless,

available information in the literature show chiral ECs to be

mostly non-racemic when present in surface waters (Table 3).

Enantiomeric fractions range broadly from 0.1 for ibuprofen

(Wang et al., 2013) to 1.0 for naproxen and ephedrine

(Kasprzyk-Hordern and Baker, 2012b; Khan et al., 2013) (an

enantiomeric fraction of 0.5 denotes a racemic mixture).

Concern arises from the large amount of toxicity testing being

undertaken with racemic mixtures (or unknown/not reported

enantiomeric composition) of the chemical under investiga-

tion (Fig. 6) (Henschel et al., 1997; Holten Lützhøft et al., 1999;

Wollenberger et al., 2000; Cleuvers, 2003; Ferrari et al., 2003;

Pro et al., 2003; Cleuvers, 2004; Eguchi et al., 2004; Pomati

et al., 2004; Cleuvers, 2005; Isidori et al., 2005a,b; Heckmann

et al., 2007; Isidori et al., 2007; Kim et al., 2007; DeLorenzo

and Fleming, 2008; Park and Choi, 2008; Quinn et al., 2008b;

De Liguoro et al., 2009; Rosal et al., 2010; Han et al., 2010;

Van den Brandhof and Montforts, 2010; Dave and Herger,

2012). However, the toxicological response and potency of

pharmaceuticals in the human body is known to differ be-

tween enantiomers of the same chemical (Table 3). For

example, the S-enantiomer of ibuprofen is 100 times more

active than R-enantiomer (Kasprzyk-Hordern, 2010). This

suggests that their toxicity to organisms within the environ-

ment will also differ. The few studies which have been

undertaken at the enantiomeric level showed significantly

different toxic responses for some aquatic taxa (Table 3)

(Stanley et al., 2006, 2007; De Andr�es et al., 2009). To demon-

strate, S-fluoxetine was found to be ~10 times more toxic to P.

promelas and T. thermophila than R-fluoxetine (Stanley et al.,

2007; De Andr�es et al., 2009). Toxiclogical effect information

gained from current ecotoxicity testing which use racemic

mixtures may therefore underestimate the risk actually posed

in the environment. Findings from racemic toxicity testing

have been used to recommend predicted effect/no effect

concentrations (Ferrari et al., 2004; Quinn et al., 2008a; Martins

et al., 2012) and can be seen as a precursor for environmental

legislation and the proposal of quality standards. Although

environmental legislation has safety buffers incorporated into

their derivation, failure to recognise the enantiomeric distri-

bution of chiral compounds in the environment and a lack of

information on the toxicological differences between enan-

tiomers is concerning.

4. Future recommendations for environmental monitoring of ECs

4.1. Sampling mode and strategy

Arguably, the most important step in monitoring for ECs in

wastewaters and in the environment is sampling. This is

fundamental to obtaining representative data. To monitor

treatment process performance for the removal of ECs, cor-

responding grab samples can be used to compensate for hy-

draulic retention time (HRT) (Petrie et al., 2013b,c; 2014c). This

approach can monitor performance of processes such as

trickling filters which operate at comparatively short HRTs

(<2 h). However, this is not practical for systems such as activated sludge which often operate at HRTs �6 h. Corre- sponding grab samples are also typically taken once daily and

do not enable the performance of the process to be fully un-

derstood. For example, pollutant removal at daily peak flows

(between 7:00 and 9:00 h) and during low flow spells (3:00 to

5:00 h) are not appreciated. Therefore a sampling approach is

needed which can: (i) obtain a composite sample representa-

tive of a system over a longer period of time (e.g., a 24 h time

period and flow or volume proportional) and, (ii) can ensure

the stability of analytes by using a suitable preservation

technique (i.e., acidification or addition of sodium azide

(Hillebrand et al., 2013)). Although collecting flow proportional

samples has logistical issues for the deployment of sampling

equipment (samplers and flow measurement devices) at

suitable locations across WwTWs or on rivers, their use is

paramount to attaining representative measurements during

environmental monitoring. Alternatively, passive samplers

could be considered but these require further investigation to

establish their suitability for the uptake of more polar chem-

icals such as ECs (Mills et al., 2014). Ideally, in situ real-time

sensors would be used. In any case sampling campaigns

should be at least one week in length to incorporate weekends

where substantial variations in flow and EC load are likely.

Furthermore, the frequency of repeat sampling campaigns

throughout the year needs consideration. A minimum of two

sampling events per year (summer/winter) to reflect the

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 7 21

dynamics of seasonal change is recommended. This will

enable seasonal usage patterns of EC to be established, as well

as the impact of temperature on WwTW performance. The

sampling strategy itself must consider attaining complete

mass balances for the WwTW or stretch of river system in

question. Analysis of waste/recycled sludge and river sedi-

ment are essential to determine fate of ECs across such sys-

tems. This includes particulate phase analysis of all sampling

positions (Petrie et al., 2014c). Admittedly this will be prob-

lematic to obtain for final effluents over a complete sampling

campaign. However, determination of final effluent particu-

late phase concentrations at least once during the sampling

campaign is valuable, considering the lack of analysis previ-

ously undertaken here.

4.2. Analysis methods

Analytical methods which can determine ECs at the enantio-

meric level are recommended. However, achieving multi-

residue separations with chiral stationary phases is difficult

due to their highly specific nature and a poor understanding of

the separation mechanism. Also, the maximum back pressure

of chiral columns is generally ~2000 psi which limits their

operation to high-performance liquid chromatography mode.

Consequently, run times are often �60 min (Kasprzyk- Hordern et al., 2010; Bagnall et al., 2012a,b; L�opez-Serna

et al., 2013), restricting sample throughput and turnover.

Stationary phases comprised of smaller particle sizes (i.e.,

<2 mm) which can achieve the performance of ultra- performance liquid chromatography (UPLC) in terms of run

time and column efficiency (Evans and Kasprzyk-Hordern,

2014) whilst attaining multi-residue enantiomeric separa-

tions would be advantageous. Until their development, the

use of comparatively fast achiral UPLC methods supported

with chiral separations to determine enantiomeric fraction for

as many chemicals as possible is suggested. Targeted UPLC

methods are capable of the simultaneous determination of up

to 100 ECs in various environmental matrices at relatively

short analysis times (~10 min) (Gracia-Lor et al., 2011; L�opez-

Serna et al., 2011; Gros et al., 2012). Ideally, these multi-

residue methods used for the analysis of ECs should be dy-

namic such that they can perform non-targeted (qualitative)

screening whilst undertaking targeted (quantitative) de-

terminations. The use of high resolution mass spectrometers

such as QTOF or Orbitrap technology which can undertake

both targeted and non-targeted screening and allow for

retrospective analysis is beneficial (Radjenovi�c et al., 2009b).

Such technology enables chemicals not originally included in

targeted screening but identified as of interest, to be easily

added for subsequent quantitative determination. The suc-

cess of non-targeted screening is reliant on good chromato-

graphic separation. Therefore the chromatography method

should be optimised for the separation of a broad range of

target ECs representing extremities of physicochemical

composition (hydrophobicity, molecular weight etc). Coupled

with screening in both negative and positive ionisation modes

will help identify unknown chemicals of notable concentra-

tion. However, non-targeted screening has several limitations

as the chemistry of the ECs in question is unknown. Therefore

they may not be recovered during sample preparation or may

not be ionised during analysis. Chemical analysis also needs

to be supported with novel bioanalytical techniques (e.g.,

metabolomics). Using a metabolomics approach can yield in-

formation on organism function and health at the molecular

level (Bundy et al., 2009). Such information would otherwise

be missed by traditional toxicological tests which rely on

endpoints such as growth, death and reproduction for a

limited number of indicator species. Long term multi-

generational studies at different trophic levels which can

simulate environmental conditions and EC concentrations are

needed. This would help establish the ecological impact of

observed EC concentrations within the environment.

4.3. Conclusions and future outlook

It is anticipated that environmental legislation will be

widened to cover a range of municipal derived ECs. However,

sound knowledge of their fate during wastewater treatment

and within the environment is currently lacking. Due to the

limitations of previously used sampling methods, reported

removals of ECs by WwTWs have uncertainties. Therefore,

removal performance of different WwTW process types at

various operational conditions needs re-evaluated with suit-

able sampling protocols. This will help establish steps

required for EC amelioration. The growing trend of improving

sustainability and reducing energy demand of wastewater

treatment will see an increase in the application of novel

treatment methods. For example, algae ponds for secondary

effluent polishing are a promising treatment method which

can indirectly produce energy through the production of

biogas. However, there are very few studies which have

monitored their performance for EC removal (De Godos et al.,

2012). Further studies of these process types are needed to

determine fate and removal of ECs during treatment, consid-

ering their likely implementation into the conventional

WwTW flow sheet. Environmental monitoring must also now

apply a holistic approach. This involves determining fate and

impact of ECs across their complete life cycle which includes

the terrestrial environment. For example, measuring biosolids

and amended soils for their occurrence is needed as well as

supporting analysis. Detailed case studies of amended soils in

field conditions which investigate leaching and runoff, impact

to surrounding surface water quality, in soil degradation,

toxicity to terrestrial organisms and the potential uptake by

plants and entry into the human food chain are needed. A

similar approach can be taken for monitoring other contam-

inated environmental compartments such as river sediments.

Finally, the combined use of chemical and biological analysis

to better assess environmental impact from ECs will enable

the revision and development of more accurate environ-

mental risk assessment.

Acknowledgements

The support of Wessex Water and the University of Bath's EPSRC Impact Acceleration Account (Project number: EP/

K503897/1) is greatly appreciated.

w a t e r r e s e a r c h 7 2 ( 2 0 1 5 ) 3 e2 722

r e f e r e n c e s

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