erratum: probabilistic application of a fugacity model to predict triclosan fate during wastewater...

e 6, Number 3—pp. 393–404
Integrated Environmental Assessment and Management — Volum � 2010 SETAC 393
Probabilistic Application of a Fugacity Model to PredictTriclosan Fate during Wastewater TreatmentMichael Bock,y* Jennifer Lyndall,z Timothy Barber,z Phyllis Fuchsman,z Elyse Perruchon,z andMarie Capdevielle§yENVIRON International Corporation, 136 Commercial Street, Suite 401, Portland, Maine 04101, USAzENVIRON International Corporation, 13801 West Center Street, Suite 1, PO Box 405, Burton, Ohio 44021, USA§Colgate-Palmolive Company, 909 River Road, Piscataway, New Jersey 08855, USA

(Submitted 14 July 2009; Returned for Revision 30 November 2009; Accepted 4 January 2010)

Erra

tum

ABSTRACTThe fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated

using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The

WWTPmodel predicts 84% to 92% triclosan removal, which is within the range ofmeasured removal efficiencies (typically 70%

to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary

treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is

40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to

biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to

variation in predicted triclosan concentrations in effluent andbiosolids include influent concentrations, solids concentrations in

settling tanks, and factors related to solids retention time.Measured triclosan concentrations inbiosolids andnon-United States

(US) effluent are consistentwithmodel predictions. However,median concentrations in US effluent are over-predictedwith this

model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection

methods)may affect triclosan removal from effluent.Model applications include predicting changes in environmental loadings

associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving

waters. Integr Environ Assess Manag 2010;6:393–404. � 2010 SETAC

Keywords: Triclosan Biosolids WWTP effluent Biodegradation Fugacity model

INTRODUCTIONTriclosan (5-chloro-2-(2,4-dichlorophenoxy)-phenol) is an

antimicrobial compound that has been widely used inpersonal health care products for more than 35 y (Waltmanet al. 2006). Many consumer products include triclosan, suchas soap, deodorant, skin cream, toothpaste, detergents, andcosmetics. In consumer products, triclosan is typically used atconcentrations ranging from 0.1% to 0.3% active ingredientby weight (Waltman et al. 2006). Additionally, triclosan isused in many household products such as clothing, counter-tops, carpets, and trash cans. Approximately 96% of theconsumer products containing triclosan are disposed throughresidential drains (Reiss et al. 2002).

The primary pathway for triclosan to enter the environmentis through municipal or industrial wastewater treatment plants(WWTPs). Approximately 74% of the United States (US)population is served by municipal WWTPs (USEPA 2000a),and 84% of the European Union population has access to urbanwastewater systems with varying levels of treatment (EC 2001).WWTPs are designed primarily to remove settleable solids,biological oxygen demand, P, and N, but are not designed toremove specific chemicals such as triclosan. Any triclosanremaining in WWTP effluent enters receiving waters, and anytriclosan remaining in the activated sludge component may beprocessed into biosolids that are subsequently applied to land

* To whom correspondence may be addressed: [email protected]

Published online 1 February 2010; erratum published 20 August 2010

DOI: 10.1002/ieam.134

(e.g., fields or forests). The aquatic and terrestrial fate andeffects of triclosan are addressed in companion publications(Fuchsman et al. 2010 [this issue]; Lyndall et al. 2010 [thisissue]). This study applies a probabilistic fugacity-based modelto evaluate the fate and partitioning of triclosan within modernWWTPs (i.e., activated sludge WWTPs with primary andsecondary treatment components). Additional applications ofthe model include predicting changes in environmental loadingsassociated with new triclosan applications and supporting riskanalyses for biosolids-amended land and effluent receivingwaters.

Previous formulations of wastewater treatment models rarelyaddress system variability. Thus, they have provided littleinformation on the impacts of differences in WWTP design andoperation on the fate of personal care products. Our analysis isintended to account for system variability in the estimation oftriclosan concentrations in effluent and biosolids. Additionally,this analysis allows an assessment of what processes andparameters have the greatest impact on the fate of triclosan ina WWTP.

METHODSA fugacity model, based on the Sewage Treatment Plant

(STP) model of Clark et al. (1995), is used to evaluatetriclosan partitioning within various WWTP compartments

Owing to a publisher’s error, the authors’ proof corrections failed

to appear in the first printing; they are shown here as they should

have appeared. Original pagination has been retained.

SECONDARY TREATMENTPRIMARY TREATMENTPRELIMINARY TREATMENTAi Fl iAi Fl i

Bar Screen

Influent

Grit Removal

G it

Solids

Primary Clarifier

Neutralization pH Adjustment

Equalization

Air FlotationAeration

Basin

Return Activated Sludge

Secondary Clarifier

Final Effluent

Waste Activated Sludge

Bar Screen

Grit Removal

G it

Solids

Primary Clarifier

Neutralization pH Adjustment

Equalization

Air FlotationAeration

Basin Secondary Clarifier

Final Effluent

Primary Sedimentation

Grit

Primary SludgeDigester Tank

Thickener

Vacuum Filter, Centrifuge, or Pressure Filter

SLUD

GE H

AN

DLIN

G

Primary Sedimentation

GritDigester Tank

Thickener

Vacuum Filter, Centrifuge, or Pressure Filter

Disposal or Processing of biosolids

Figure 1. Modeled wastewater treatment plant process.

394 Integr Environ Assess Manag 6, 2010—M Bock et al.

(Figure 1). The original STP model predicts the removal of achemical in a WWTP based on default point estimate valuesthat the authors determined were representative values forCanadian WWTPs. Trent University updated the original STPequations to increase clarity. These updated equations anddescriptions can be found in the help files of the currentversion of the STP model (http://www.trentu.ca/cemc).

The model described herein is based on the same processesand equations as the updated STP model but incorporatessystem variability by using a probabilistic approach. Thus, themodel is designed to predict the range of conditions andremoval rates associated with treatment in a WWTP. In eachiteration of the STP model, triclosan enters the WWTP withinfluent at a constant loading rate and goes to steady statethrough biodegradation, sorption, and/or other loss mecha-nisms (in effluent or through volatilization) from eachWWTP compartment. Model outputs include total removaland effects of the 3 contributing processes (biodegradation,sorption to sludge, and volatilization and/or effluent removal)based on the range of typical operating conditions in a WWTP(Clark et al. 1995; Qasim 1999; Spellman 2003).

For the purposes of this model, sorption and biodegrada-tion processes are limited by the residence time within thegiven WWTP compartments. Thus, while biodegradation oftriclosan may continue in wasted sludge, only the portion thathad biodegraded during the compartment residence time isconsidered in the model. The WWTP model only simulatesprocesses through the secondary settling tank; subsequenttreatment of wasted sludge (e.g., aerobic or anaerobicdigestion, composting) is not considered. Similarly, advancedeffluent treatments such as disinfection are not simulated.Triclosan is ionizable with a pKa of approximately 8.0(Budavari 1989; Jakel 1990); however, only a smallfraction is ionized within the normal pH range in WWTPs.Therefore, this evaluation only addresses the neutral speciesof triclosan.

Model formulation

The model is written in fugacity format as described byClark et al. (1995) and Mackay (2001). Fugacity is theequivalent of a partial pressure of a substance within a givenphase and can be used to predict chemical partitioning amongvarious phases. A full accounting of fugacity is beyond thescope of this document; readers are referred to the originalSTP paper (Clark et al. 1995) or Mackay (2001) and thematerials at http://www.trentu.ca/cemc. The following is abrief summary of the STP model.

There are 3 sets of intermediate parameters (fugacitycapacity [Z], partitioning coefficients [K], and fugacity rateparameters [D]) that must be calculated before calculatingfugacity (f). In the STP model, the Z values for eachcompartment (air, water, and biomass) are calculatedaccording to the following equations:

Zair ¼ 1=ðR � TÞ ð1Þ

Zwater ¼ 1=H ð2Þ

Zbiomass ¼ ð0:2 � Kow � ZwaterÞ þ ð0:8 � ZwaterÞ ð3Þ

where R¼ gas constant (Pa m3/mol K), T¼ temperature (K),H¼Henry’s Law constant (Pa m3/mol), and biomass is theequivalent of both the sludge and microbial communitycompartments. The partitioning coefficients (K) between eachcompartment can be calculated using the following formulae:

Kaw ¼ H=ðR � TÞ ð4Þ

Kbw ¼ Zbiomass=Zwater ð5Þ

where Kaw¼ the partition coefficient between air and water,and Kbw¼ the partition coefficient between biomass andwater. Volatilization rate is expressed as an overall mass

Table 1. Physicochemical properties of triclosan

Property Value Source Internally Consistent Valuea

Henry’s Law constant 4.99�10�9 atm�m3/mole@ 25 8C

Meylan and Howard 1993 2.27�10�8 atm�m3/mole@ 25 8C

Log n-octanol/waterpartition coefficient (log KOW)

4.78 L/kg Wyler and Jakel 1990 4.76 L/kg

Vapor pressure 4�10�6 mm Hg @ 20 8C Budavari 1989 1.8�10�4 Pa @ 25 8C

6.45�10�7 mm Hg @ 25 8C Neely and Blau 1985

7.00�10�4 Pa @ 25 8C Ciba Specialty Chemical 2004

Solubility in water 10 mg/L @ 25 8C Yalkowsky and Dannenfelser 1992 23.4mg/L

12 mg/L @ 20 8C Pointurier and Volz 1990

Dissociation constant (pKa) 8.14 @ 20 8C Jakel 1990 Not calculated

7.9 Budavari 1989

aCalculated according to Beyer et al. (2002). Average measured values for vapor pressure and water solubility were given twice the weight of values for Henry’s

Law constant and log Kow, based on data quantity and quality.

Triclosan and Wastewater Treatment—Integr Environ Assess Manag 6, 2010 395

transfer coefficient (Kv), calculated as:

1=Kv ¼ 1=Kw þ 1=ðKa � KawÞ ð6Þ

where Kv¼ the overall partitioning coefficient, Ka¼ the airside mass transfer coefficient (Table 1), and Kw¼ the waterside mass transfer coefficient (Table 1).

The partitioning coefficients are then used to calculate thefugacity rate parameters (D1 to D9) for transfer, degradation,and vaporization among compartments (Figure 1). D valuesfor transport, degradation, and volatilization are used. Fortransport from one compartment to another:

D ¼ GZ ð7Þ

where G¼ the flow rate (m3/h) of the phase (i.e., water,sludge, air), defined through mass balance calculations basedon the influent flow rate and solids concentration (Clark et al.1995). A transfer D value is calculated for multiple flowstreams: influent (D1), primary tank to aeration tank (D2),primary tank to primary sludge (D3), inflow from air toaeration tank (D4), aeration tank to air (D5), aeration tank tosettling tank (D6), settling tank to effluent (D7), settling tankto aeration tank by return sludge (D8), and settling tank towaste sludge (D9). For degradation in a compartment:

D ¼ VZk ð8Þ

where V¼ the phase volume (m3), and k is a first-order rateconstant (h�1) calculated from the triclosan half-life.

Finally, the fugacity for each WWTP tank is calculatedusing the following equations:

fp ¼ E=ðD2 þ D3 þ Dpv þ DpbÞ ð9Þ

fa ¼ D2fp=½D5 þ D6 þ Dab�ðD8D6Þ=D7 þ D8 þ D9

þ Dsv þ Dsb ð10Þ

fs ¼ D6fa=ðD7 þ D8 þ D9 þ Dsv þ DsbÞ ð11Þ

where fp¼ fugacity in the primary tank, E¼ influx of chemicalinto the plant (mol/h), D2. . .D9¼ fugacity rate parameter, as

described above, Dpv¼ the volatilization D value for primarytank, Dpb¼ the biodegradation D value for the primarytank, fa¼ fugacity in the aeration tank, Dab¼ the biodegrada-tion D value for the aeration tank, Dsv¼ the volatilization Dvalue for settling tank, Dsb¼ the biodegradation D value forthe settling tank, and fs¼ fugacity in the settling tank. Knowingf and Z, the concentration (C) of a substance (mol/m3) in eachcompartment can be calculated by the formula:

C ¼ fZ ð12Þ

Model parameterization

The STP model was modified to accommodate probabil-istic input parameters simulating the range of WWTPoperating conditions and was run as a Monte Carlo simulationwith 100 000 iterations. The model assumptions andequations are unchanged from those found in Clark et al.1995 and the materials at http://www.trentu.ca/cemc. Themodified model incorporates a combination of deterministicand probabilistic parameters (Table 2). Influent flow rate isheld constant for modeling purposes, because treatmentprocesses are scaled to influent volume, such that concen-trations in effluent and biosolids are not affected. Pointestimate inputs are also used for chemical constants andcertain tank dimensions. The chemical properties oftriclosan were determined based on structure activityrelationships, direct measurements, and the consistency ofthe parameters to each other. It has been observed thatmeasurement errors in chemical properties can be significant(Beyer et al. 2002), resulting in physiochemical parametersthat are not thermodynamically consistent with each otherand, therefore, cannot be correct. Beyer et al.’s (2002)methods were used to mathematically determine internallyconsistent physicochemical properties for triclosan (Table 1).In so doing, vapor pressure and water solubility wereweighted twice as heavily as the other parameters, becausemultiple high quality data sets were available for theseproperties.

Table 2. Modified STP model input parameters

Parameter Distribution Values Units Source

PhysicochemicalProperties

Kow Point 5.75�104 L/kg Internally consistent valuebased on Beyer et al. 2002

R (gas constant) Point 8.314 Pa m3/mol K

Henry’s Law Constant Point 2.27�10�3 Pa m3/mol K Internally consistent valuebased on Beyer et al. 2002

Molecular Weight Point 289.54 g/mol Calculated

Temperature Point 25 8C Clark et al. 1995

Density of Biosolids Point 1000 kg/m3 Clark et al. 1995

Kw Point 0.05 m/h Clark et al. 1995

Ka Point 5 m/h Clark et al. 1995

Half-Life(2000mg/LMLSS)

Aeration and SettlingTanks

Normal Min 10 h Based on 28-d treatability of50–70% (Hanstveit and Ham-wijk 2003; Stasinakis et al.2008) and corresponding

USEPA (2000b) recommendedvalues þ/- 50%

Mean 30 h

SD 7.5 h

Primary Tank Normal Calculated h Aeration tank half-life�10(Clark et al. 1995)

Influent Influent Flow rate Point 1000 m3/h Clark et al. 1995

TSS Normal Min 45 g/m3 Range of values from Qasim1999 (90–280, mean¼175),min¼1/2 lower range, SDcalculated assuming upperrange value is the 95th per-

centile

Mean 175 g/m3

SD 52.5 g/m3

Triclosan (U.S.) Lognormal Geo. Mean 4.79 mg/L Fitted from measured con-centrations

Geo. SD 1.91 mg/L

Min 0.05 mg/L

Triclosan (non-U.S.) Lognormal Geo. Mean 1.03 mg/L Fitted from measured con-centrations

Geo. SD 4.15 mg/L

Min 0.05 mg/L

Primary Treat-ment

Primary Tank Area Point 266.7 m2 Clark et al. 1995

Primary Tank Depth Point 3.8 m Clark et al. 1995

Fraction of InfluentTSS Removed

Uniform Min 0.4 proportion Qasim 1999; Spellman 2003

Max 0.6 proportion


Parameter Distribution Values Units Source

Fraction of TankVolume as VSS

Normal Min 0.0005 proportion Qasim 1999; Spellman 2003

Mean 0.0050 proportion

SD 0.0015 proportion

Sludge VSS Point 50000 g/m3 Clark et al. 1995; Qasim 1999;Spellman 2003

Aeration Tank Aeration Tank Area Uniform Min 500 m2 Varied to result in retentiontime from 4 to 8h (Qasim1999; Spellman 2003)

Max 1000 m2

Aeration Tank Depth Point 8 m Qasim 1999, Spellman 2003


Normal Calculated proportion 0.0025 x multiplier (Clarket al. 1995; Spellman 2003)

Outflow VSS Normal Calculated g/m3 2500 x multiplier (Clark et al.1995; Spellman 2003)

Multiplier (for VSScalculations)

Normal Min 0.1 proportion Based on range of VSS values(Qasim 1999; Spellman 2003)

Mean 1 proportion

SD 0.3 proportion

Secondary Set-tling Tank

Settling Tank Area Point 727.3 m2 Clark et al. 1995

Settling Tank Depth Point 3.8 m Clark et al. 1995


Normal Calculated proportion 0.00055 x multiplier (Clarket al. 1995; Spellman 2003)

Sludge VSS Normal Calculated g/m3 5500 x multiplier (Clark et al.1995; Spellman 2003)

Outflow VSS Normal Calculated g/m3 10 x multiplier (Spellman2003)

Fraction of FlowRecycled

Uniform Min 0.5 proportion Clark et al. 1995; Qasim 1999;Spellman 2003

Max 0.975 proportion

Fraction of FlowRemoved

Uniform Min 0.005 proportion Clark et al. 1995; Qasim 1999;Spellman 2003

Max 0.025 proportion

LSS mean live suspended solids.

SS total suspended solids.

SS volatile suspended solids.

Table 2. (Continued)


M

T

V

Probabilistic input parameters describe the range of solidsloading and processing parameters and tank dimensions inactivated sludge WWTPs in the United States, based onindustry standard design and operating criteria (Clark et al.1995; Qasim 1999; Spellman 2003). Probabilistic parametersinclude the influent solids load, the fraction of influentsuspended solids removed in the primary tank, aeration tankvolume, concentrations of volatile suspended solids (VSS)

(i.e., the organic component of suspended solids) in the tanksand tank return, outflow of VSS, sludge removal from thesecondary settling tank, and flow recycle from the secondarysettling tank to the aeration tank. The variability in theseparameters primarily impacts the mechanical removal ofsolids and the solids retention time. Biodegradation isexpected to be directly related to solids retention time(Ternes et al. 2004).

Table 3. Measured triclosan concentrations in WWTP influent and removal efficiencies from WWTP systems

Location

InfluentConcentration

PrimaryEffluent

SecondaryEffluent

FinalEffluent

SourceMean (Range)

(mg/L)Removala

(%)Removal

(%)Removal

(%)

Activated Sludge

Crofton, UK 21.9 39 – 95 Sabaliunas et al. 2003

Columbus, Ohio, USA 5.2 35 – 95 McAvoy et al. 2002

Loveland, Ohio, USA 10.7 36 – 96 McAvoy et al. 2002

Rhine-Ruhr Region, Germany 7.3 – – 96 Bester 2005

Rhine-Ruhr Region, Germany 4.8 31 63 87 Bester 2005

Dortmund, Germany 1.2 (1.1–1.3) – – 96 Bester 2003

Denton, Texas 7.3 (2.7–26.8) – – 98 Waltman et al. 2006

Arlington, Virginia 3 10 87 98 Thomas and Foster 2005

Alexandria, Virginia 3.3 45 53 99 Thomas and Foster 2005

Fairfax Country, Virginia 3.6 17 82 99 Thomas and Foster 2005

Almerıa, Spain 1.8 (0.4–4.2) – – 89 Gomez et al. 2007

Mid-Atlantic Region, USA 4.7 (0.8–10.8) – – 99 Heidler and Halden 2007

Ontario, Canada 1.9 (0.01–4.0) – – 94 Lishman et al. 2006

Tokyo, Japan 0.5 – – 45–93 Nakada et al. 2006

Lyon, France 0.38 – – 55 Paxeus 2004

Patras, Greece 2.17 – – 94 Paxeus 2004

Naples, Italy 1.37 – – 73 Paxeus 2004

Lund, Sweden 0.38 – – 58 Paxeus 2004

Copenhagen, Denmark 0.09 – – 88 Paxeus 2004

Adelaide, Australia 0.85 41 41 93 Ying and Kookana 2007

Perth, Australia 0.58 7 59 71 Ying and Kookana 2007

Coslech, South Wales, UK 0.23 (0.03–0.46) – – 75 Kasprzyk-Hordern et al. 2009

Moorhead, Minnesota, USA 2.21 b 87 91 Shelver et al. 2007

Trickling Filter

Meltham, UK 7.5 21 – 95 Sabaliunas et al. 2003

Glendale, Ohio, USA 3.8 7 – 58 McAvoy et al. 2002

West Union, Ohio, USA 16.6 – 86 86 McAvoy et al. 2002

West Union, Ohio, USA 15.4 48 34 83 McAvoy et al. 2002

United Kingdom (Biological filter) 1.55 (1.0–2.1) 39–46 63–88 89 Thompson et al. 2005

Cilfynydd, South Wales, UK 0.087 (<0.097–0.24) – – 71 Kasprzyk-Hordern et al. 2009

Fargo, North Dakota, USA 2.56 b 72 92 Shelver et al. 2007

Other WWTPs

United Kingdom (Rotatingbiological contactor)

2.5 (1.3–3.7) 40–89 25–30 75 Thompson et al. 2005

United Kingdom (Oxidation ditch) 3.35 (1.6–5.1) – 97 98 Thompson et al. 2005


Location

InfluentConcentration

PrimaryEffluent

SecondaryEffluent

FinalEffluent

SourceMean (Range)

(mg/L)Removala

(%)Removal

(%)Removal

(%)

Adelaide, Australia (Lagoon) 0.59 – – 85 Ying and Kookana 2007

Adelaide, Australia (Bioreactor) 0.81 – 85 89 Ying and Kookana 2007

Adelaide, Australia (Oxidation ditch) 0.79 – 91 92 Ying and Kookana 2007

West Fargo, North Dakota, USA (Lagoon) 4.12 – – 98 Shelver et al. 2007

aRemoval percentages for each treatment phase are compiled from multiple WWTPs at multiple time points and therefore should not be considered additive.bPrimary effluent concentration exceeded influent concentration.

Influent100

Fitted Distribution - U.S.

60

80Fitted Distribution - Non-U.S.Measured - U.S.Measured - Non-U.S.

10001001010.10.010

20

40

Triclosan(µg/L)

Cum

ulat

ive

Perc

enta

ge o

f In

fluen

t Con

cent

ratio

ns

Effluent

60

80

100Modeled - U.S.Modeled - Non-U.S.Measured - U.S.Measured - Non-U.S.

20

40

Cum

ulat

ive

Perc

enta

ge o

f Ef

fluen

t Con

cent

ratio

ns

Biosolids

80

100Modeled - U.S. Modeled - Non-U.S.Measured - U.S. Measured - U.S. Non Detect

1001010.10.010.0010

Triclosan (µg/L)

40

60

Measured - Non-U.S.

Cum

ulat

ive

Perc

enta

ge o

f B

ioso

lids C

once

ntra

tions

Triclosan (mg/kg dry weight)

10001001010.10.010

20

a

b

c

Figure 2. Comparison of measured triclosan concentrations with fitted

distributions (influent) and modeled concentrations (effluent and biosolids).

Table 3. (Continued)


The activated sludge half-life for triclosan is estimated frompublished laboratory biodegradability test results, extrapo-lated to in-plant conditions following recommendations ofUSEPA (2000b). As reviewed by NICNAS (2009), triclosandegradation in 28-d laboratory tests with relevant initialconcentrations (i.e., excluding unrealistically high concen-trations potentially toxic to activated sludge microbes) rangesfrom 50–70% (Hanstveit and Hamwijk 2003; Stasinakis et al.2008). According to USEPA (2000b), 28-d degradation of20–70% corresponds to an in-plant activated sludge half-lifeof 30 h, whereas �70% degradation corresponds to an in-planthalf-life of 10 h. We represent the uncertainty of thisderivation by assuming a half-life of 30 h plus or minus 50%.

For modeling purposes, triclosan loading is estimated using2 approaches. To quantify removal efficiencies, influentloading is set to 1 gram of triclosan per h to simplifycalculations. To estimate triclosan concentrations in biosolidsand effluent, a distribution of triclosan concentrations ininfluent is fitted from published measurements collectedinternationally (Table 3; Figure 2), using the best-fit functionof Crystal Ball1. Separate influent distributions are identifiedfor US and non-US WWTPs. This facilitates comparison tomeasured triclosan concentrations in biosolids, because nearlyall (97%) of the biosolids data compiled for this purpose arefrom US sources (Table 3).

Model calibration

The model is calibrated to measured triclosan concen-trations in effluent and biosolids. Effluent data were compiledfrom the sources listed in Table 3 and from Boyd et al.(2003), Lindstrom et al. (2002), and Sabaliunas et al. (2003).Biosolids data sources are summarized in Table 4; informationon WWTP type and biosolids treatment processes (e.g.,digestion, composting) is not available for most of thebiosolids measurements. To weight each WWTP equally,distributions of measured triclosan concentrations in effluentand biosolids are based on mean concentrations fromindividual WWTPs.

RESULTS AND DISCUSSIONThe model’s output is summarized in Table 5 and Figures 2

and 3. Triclosan concentrations in WWTP influent are higherin the United States than in other developed countries(Figure 2), consistent with higher triclosan usage in theUnited States (MacAvoy et al. 2002; Singer et al. 2002).Triclosan concentrations in non-US effluent are consistent

Table 4. Summary of data sources for measured triclosan concentrations in biosolids

LocationNumber ofWWTPs

Range(dry mg/kg)

Median(dry mg/kg) Reference

United States (mid-Atlantic region) 1 20.0–55.0 37.5 Heidler and Halden 2007

United States (AZ, CO, IA, KS, TX, WA, WI) 9 0.4–10.5 2.6 Kinney et al. 2006

United States (VA, NY, MD, CA) 6 3.6–7.4 5.4 La Guardia et al. 2004

United States (OH) 3 0.5–15.6 1.5 McAvoy et al. 2002

New Zealand 1 1.0–22.0 11.5 Speir and Northcott 2006

United States (WA) 1 23.0–29.0 26.0 USEPA 2003; Reimer 2003

United States 73 0.33–34.0 7.2 USEPA 2009a

Australia 2 0.2–5.6 2.9 Ying and Kookana 2007

aThe maximum reported triclosan concentration (133mg/kg) is excluded as an outlier, based on a data qualifier indicating possible matrix interference, as well as

lack of agreement with other measured concentrations.


with model predictions based on non-US influent data in thatthe medians are within 15%. However, the median triclosanconcentration in US effluent is over-predicted in this modelby 80% based on median US influent data (Figure 2). Thisresult might possibly reflect differences between the UnitedStates and other countries in effluent treatment processessuch as disinfection. Triclosan is effectively removed throughoxidation by disinfection agents including free Cl, chlor-amines, and ozone, whereas ultraviolet disinfection showslimited removal of microcontaminants (Snyder et al. 2008).However, documentation of different disinfection practicesinternationally is limited, and chlorination remains in wide-spread use (Jacangelo and Trussell 2002); thus, any suchexplanation is highly speculative. Artifacts of the effluent dataset (e.g., biases associated with inclusion of multiple WWTPtypes) do not appear to explain the observed results. Thepredicted and modeled biosolids concentrations for theUnited States are within 5% of each other. For non-USbiosolids concentrations, modeled and measured concentra-tions cannot be reliably compared due to small sample size(n¼ 3).

Table 5. Modeled triclosan concen

Percentile

US Influent SourceNon-US Influent

Source

Effluent(mg/L)

Biosolids(dry mg/kg)

Effluent(mg/L)

Biosolids(dry mg/kg) Bi

5 0.17 1.72 0.01 0.13

10 0.21 2.22 0.02 0.21

20 0.29 3.03 0.04 0.37

50 0.53 5.53 0.12 1.22

80 0.95 10.12 0.39 4.14

90 1.31 14.04 0.73 7.79

95 1.71 18.39 1.23 13.14

Interestingly, although not predicted by the model,measured triclosan removal efficiency may be related toinfluent concentration, with low influent concentrationsassociated with a wide range of removal efficiencies andhigher influent concentrations generally associated with highremoval efficiencies (Figure 4). The Spearman’s rank corre-lation coefficient for this relationship is 0.54 (p< 0.001).However, the correlation for US data is not significant(rho¼�0.21, p> 0.05). For non-US data, the correlationcoefficient is 0.71 (p< 0.001). This does not necessarilyreflect a true concentration-dependent removal mechanism,however, and could be a spurious correlation or a relationshipto some other factor that co-varies with triclosan influentconcentration in non-US treatment facilities.

The removal efficiency of the modeled WWTP (median of89%) is similar to measured removal efficiencies (median of93%). In both the modeled system and the measuredWWTPs, approximately 1/3 of triclosan is removed duringprimary treatment, while secondary treatment removes themajority of triclosan (Tables 2 and 4). The 2 primarymechanisms for triclosan removal are sorption to solids and

trations and removal efficiencies

Removal Efficiency (%)

Primary Secondary

odegradation Sorption Biodegradation Sorption Total

2 23 31 4 82

3 25 33 5 84

3 27 35 6 86

4 31 40 9 89

6 36 44 12 91

7 38 46 14 92

7 40 48 15 93

Figure 3. Relative frequency distributions of model output: solids retention

time (a), overall triclosan removal due to biodegradation and sorption (b), andoverall removal efficiency (c).


biodegradation (in aqueous and solid phases). The overallmodeled triclosan removal efficiency is 84% to 92% (10th to90th percentile) during WWTP passage. Volatilization isnegligible in all treatment compartments because of the verylow Kaw of 9.17� 10�7. Overall, approximately 30% to 52%(10th to 90th percentile) of triclosan is sorbed to sludge and36% to 53% (10th to 90th percentile) is biodegraded(Table 6). The modified STP model results are similar tothose reported based on mass balance (Bester 2003; Thomp-son et al. 2005; Heidler and Halden 2007). However, themodel estimates higher sorption and lower degradation than

100

80

90

60

70

Non-U.S.Activated Sludge

U.S. WWTP Type

Rem

oval

Effi

cien

cy (%

)

Filter

502520151050

Concentration (µg/L)Influent Triclosan

TricklingOther

Figure 4. Concentration dependence of measured triclosan removal

efficiency.

the KOC partitioning estimate by Singer et al. (2002). Thisdiscrepancy is likely because Singer et al. (2002) beganmeasuring triclosan after the influent was mechanicallyclarified. As a result, sorption in the primary treatment phaseis underrepresented.

Evaluation of WWTP processes

The STP model indicates that sorption and degradation areeach dominant in different WWTP treatment compartments(Table 5). Of the overall triclosan removal due to sorption(median¼ 40%), the model confirms that the majority of theremoval occurs in the primary treatment phase(median¼ 31%), when settleable solids are removed. Secon-dary treatment accounts for the remainder of sorption(median¼ 9%), as triclosan sorbs to activated sludge in thisphase. In comparison, the overall triclosan removal due tobiodegradation (median¼ 48%) is relatively limited duringprimary treatment (median¼ 4%) but is dominant duringsecondary treatment (median¼ 44%). Based on these com-partmental trends, it can be concluded that sorption is the keyremoval mechanism during primary treatment, while biode-gradation is dominant during secondary treatment.

The relative contribution of biodegradation during secon-dary treatment is influenced by multiple factors, includingdissolved oxygen, solids retention time, biodegradation half-life, and solids loading. Thompson et al. (2005) reported thatsorption appears to be the main mechanism of triclosanremoval at lower dissolved oxygen levels (e.g., rotatingbiological contactor and trickling filter treatment methods),while biodegradation is dominant when a higher oxygen level(dissolved oxygen level >1.5 mg/L) is maintained. This isconsistent with observations that triclosan degradation inanaerobic systems is much slower than in aerobic systems(Christensen 1994a, 1994b).

Solids retention time is considered a key parameteraffecting the removal of a variety of pharmaceutical andpersonal care product chemicals from wastewater (e.g., Claraet al. 2005). Adam (2006) reported that the WWTP processentails high initial triclosan sorption to solids followed bydegradation over time within the solid phase. Federle et al.(2002) reported that more than 94% of triclosan wasultimately biodegraded in a laboratory experiment designedto simulate secondary treatment in an activated sludgeWWTP. Similarly, Stasinakis et al. (2007) found that 97%of triclosan was ultimately biodegraded in a 96-d activatedsludge WWTP. Thus, the amount of biodegradation occurringin a WWTP is a function of solids retention time rather thanthe partitioning of triclosan into a recalcitrant fraction that isresistant to biodegradation.

In the model, solids retention time is a function of aeratedsludge volume (set based on STP defaults and standardpractices) and aerated sludge removal rates (determined bythe STP equations). Therefore, solids retention time is amodel output. The modeled solid retention time ranged from1 to 66 d, with a median solids retention time ofapproximately 10 d (Figure 3). The model output demon-strates a correlation between the solids retention time andoverall triclosan removal (Spearman’s rank correlation,p< 0.001; n¼ 10 000; rho¼ 0.30) (Figure 5). This relation-ship is modeled using Michaelis-Menton kinetics:

Fdeg ¼ ða � TrÞ=ðb ¼ TrÞ ð13Þ

Table 6. Reported relative contributions to triclosan removal from wastewater treatment plant systems

Method Type of System Biodegradation (%) Sorption (%) Source

Mass balance Activated sludge 65 30 Bester 2003

Mass balance Activated sludge 48 50 Heidler and Halden 2007

Mass balance Activated sludge 79a 15a Singer et al. 2002

Mass balance Fixed film – 40–90 Thompson et al. 2005

Fugacity model Activated sludge 36–53b 30–52b This study

aMeasurements do not include primary treatment and therefore underestimate sorption to sludge.bRange based on 10th and 90th percentiles from model.


where Fdeg is the proportion of triclosan biodegraded, Tr is thesolids retention time, a is the maximum proportion biode-graded, and b is a constant (a¼ 0.71; b¼ 0.31). The equationpredicts that for increasing solids retention times, up toapproximately 10–15 d, there is an increase in triclosanbiodegradation. Indeed, upgrading WWTPs to achieve a solidsretention time of 12–15 d has been recommended to improveremoval of a variety of chemicals commonly found inwastewater (Ternes et al. 2004).

Parameter contributions to model variability

Sensitivity analysis tools in Crystal Ball1 quantify theinfluence of probabilistic model parameters on modelpredictions. When a distribution is used to describe theinfluent concentration of triclosan, it is the most importantparameter by a wide margin, accounting for approximately80% of the variability in the effluent and biosolids triclosanconcentrations. Thus, the predictions of the model are verysensitive to the influent concentrations, and biases in thisparameter potentially have a significant impact on the results.One source of uncertainty is the sensitivity of the fittedinfluent concentration to small changes in the influent dataset.The sensitivity of the geometric mean to individual observa-tions was explored using piecewise elimination of single

70

80

Prop

ortio

n Bi

odeg

rade

d

40

50

60

Biosolids Retention Time (Days)

40302010

Figure 5. Proportion of triclosan predicted to be biodegraded as a function of

calculated solids retention time in wastewater treatment plant aeration and

settling tanks during secondary treatment. Points represent individual model

iterations. Dashed line represents the nonlinear regression: y¼0.71x / (x þ0.31).

influent data points. The elimination of individual valuesresulted in changes in the geometric mean of 50% or less,meaning that the geometric mean of input distribution islikely to be within 50% of the true value, considerably lessthan the range of 3 orders of magnitude in the observedinfluent concentrations. Therefore, the magnitude of theuncertainty associated with the fitted influent data is minor incomparison to the variability associated with using adistribution of influent concentrations.

When the variability associated with triclosan loading ininfluent is controlled by using a single influent concentrationfor all model runs, the impacts of the variability in otherprobabilistic parameters can be assessed. Variations ineffluent and biosolids concentrations are primarily linked tothe factors controlling sorption to wasted sludge andbiodegradation. The most important factor for triclosansorption is the VSS concentration in the settling tanks.Variation in VSS concentration accounts for approximately60% of the variation in effluent concentrations and 70% of thevariation in the biosolids concentration of triclosan. Amongthe variables related to solids retention time, the aeration tankarea is the most important contributor to predicted variationin triclosan concentrations, accounting for 20% of thevariation in effluent and 15% of the variability in biosolids.Other important factors controlling biodegradation includethe rate of solids wasting from the secondary tank and the rateof biosolids recycling back to the aeration tank, collectivelyaccounting for 10% of the variability in the effluent andbiosolids concentrations. Of these parameters, the aerationtank area represents a WWTP design consideration that couldbe adjusted to maximize the removal efficiency of a plant.The other parameters are generally adjusted to maximize theremoval efficiency of biological oxygen demand, N, andphosphate (Qasim 1999; Spellman 2003), which typicallygovern the design and operation of a WWTP. The activatedsludge half-life of triclosan, although uncertain, contributesnegligibly to the variation in model output.

When all other parameters are held constant and theaeration tank volume is set to 4000, 6000, and 9000 m3,biosolids retention times are 5.2, 7.5, and 9.7 days; thebiodegradation removal efficiency is 62%, 70%, and 75%,resulting in total removal efficiencies of 87%, 90%, and 92%.Clearly, increasing the size of the aeration tank can havea dramatic impact on biodegradation and, therefore, theconcentration of triclosan in biosolids. Increasing the aerationtank volume from 4000 to 9000 m3 results in a 35% reductionin the triclosan concentration in biosolids.


CONCLUSIONSWastewater treatment processes remove triclosan to a high

degree of efficiency by sorption to solids and biodegradation.Removal by sorption to solids is dominant in the WWTPprimary treatment phase as solids settle out. Biodegradation isdominant in the WWTP secondary treatment phase, wherehigh dissolved oxygen concentrations and longer solidsretention times can further enhance biodegradation of bothaqueous and sorbed triclosan. Our probabilistic modificationof the STP model effectively predicts the distribution oftriclosan concentrations in biosolids and non-US effluent, butconcentrations in US effluent are over-predicted. Furtherinvestigation is warranted to identify treatment practices bywhich US WWTPs achieve low triclosan concentrations ineffluent, despite higher influent concentrations than in othercountries.

Acknowledgment—An expert panel consisting of DonMackay, Lawrence Barnthouse, and Michael C. Newmanprovided thoughtful reviews of an earlier version of this study,resulting in substantial improvements to the analysis. We alsothank two anonymous reviewers for their thoughtful com-ments. Kannan Vembu provided assistance in understandingWWTP design and processes. Thanks also to Michael Fergusonfor model quality control. Colgate-Palmolive Companyfunded preparation of this manuscript.

Disclaimer—The peer-review process for this article wasmanaged by the Editorial Board without involvement of Edi-tor-in-Chief R. Wenning.

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erratum: probabilistic application of a fugacity model to predict triclosan fate during wastewater...

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