Micropollutant Biotransformation Kinetics Associate with WWTP

Article pubs.acs.org/est

Micropollutant Biotransformation Kinetics Associate with WWTP Process Parameters and Microbial Community Characteristics Damian E. Helbling,†,* David R. Johnson,†,‡ Mark Honti,† and Kathrin Fenner†,‡ †

Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland Department of Environmental Systems Science (D-USYS), ETH Zürich, 8092 Zürich, Switzerland

‡

S Supporting Information *

ABSTRACT: The objective of this work was to identify relevant wastewater treatment plant (WWTP) parameters and underlying microbial processes that influence the biotransformation of a diverse set of micropollutants. To do this, we determined biotransformation rate constants for ten organic micropollutants in batch reactors seeded with activated sludge from ten diverse WWTPs. The estimated biotransformation rate constants for each compound ranged between one and four orders of magnitude among the ten WWTPs. The biotransformation rate constants were tested for statistical associations with various WWTP process parameters, amoA transcript abundance, and acetylene-inhibited monooxygenase activity. We determined that (i) ammonia removal associates with oxidative micropollutant biotransformation reaction rates; (ii) archaeal but not bacterial amoA transcripts associate with both ammonia removal and oxidative micropollutant biotransformation reaction rates; and (iii) the activity of acetylene-inhibited monooxygenases (including ammonia monooxygenase) associates with ammonia removal and the biotransformation rate of isoproturon, but does not associate with all oxidative micropollutant biotransformations. In combination, these results lead to the conclusion that ammonia removal and amoA transcript abundance can potentially be predictors of oxidative micropollutant biotransformation reactions, but that the biochemical mechanism is not necessarily linked to ammonia monooxygenase activity.

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INTRODUCTION Wastewater treatment plants (WWTPs) are designed to remove organic carbon, nitrogen, and phosphorus loads from domestic, commercial, and industrial wastewater streams. Over the past decade or more, data has shown that influent wastewater streams also contain a variety of micropollutants including pharmaceuticals, personal care products, detergents, and other down-the-drain chemicals.1,2 Much recent work has focused on which types of micropollutants are biologically removed (or biotransformed) in WWTPs and how different WWTPs perform in terms of biotransformation capacity.3,4 An important conclusion from these studies is that individual micropollutants are biotransformed to varying extents among different WWTPs. This observation leads to a critically important research question: why do some WWTPs more effectively biotransform micropollutants than others? Addressing this question is imperative to enable strategies that minimize the introduction of micropollutants into receiving surface waters and the urban water cycle. In addressing this question, others have focused on WWTP parameters that may enhance the biotransformation of micropollutants. These include laboratory- and field-based studies that have investigated associations between micropollutant removal and (among others) physicochemical parameters such as temperature,5,6 operating parameters such as the hydraulic (HRT)7 and solids retention times (SRT),8,9 © 2012 American Chemical Society

and properties of the activated sludge community such as biomass activity10 and nitrification potential.11,12 This body of work has contributed to our understanding of WWTP-specific properties that may influence micropollutant biotransformations, but the majority of these studies have focused on only one or a few WWTPs and only a few structurally similar compounds. Generalized results that could have practical impact have therefore remained elusive. Further, any associations identified between micropollutant biotransformation and WWTP parameters are merely surrogate metrics for an underlying microbiological process. For example, the observation of enhanced micropollutant biotransformation in nitrifying activated sludge has been attributed to ammonia monooxygenase (AMO) and ammonia oxidizing bacteria (AOB), a hypothesis that has been tested in pure cultures13−15 and in wastewater-derived communities with the use of AMO inhibitors.13,14,16−18 Though it has been shown that biotransformation reactions of some micropollutants can be catalyzed by AMO,13,14,16−18 negative results have also been reported13,19 and it remains unclear what the relative contribution of AMO is to general micropollutant biotransforReceived: Revised: Accepted: Published: 10579

May 15, 2012 August 31, 2012 August 31, 2012 September 1, 2012 dx.doi.org/10.1021/es3019012 | Environ. Sci. Technol. 2012, 46, 10579−10588

Environmental Science & Technology

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set of biotransformation reactions, the expected initial reactions can be grouped into two general categories: substitution reactions and oxidation reactions. Spike mixtures including the ten micropollutants were prepared in methanol at individual micropollutant concentrations of 100 mg/L. Standard mixtures used for quantification containing all parent compounds and transformation products listed in Table 1 were also prepared in methanol.

mation compared to enzymes from heterotrophic microorganisms. Coupling studies that first investigate associations between micropollutant removal and WWTP-specific parameters with those that investigate the relevant microbial processes could yield insight into the fundamental mechanisms involved in these reactions. Further characterization of the genetic content (genes and transcripts) and/or enzyme activity of activated sludge communities could provide stronger links between WWTP parameters and underlying microbial processes. Quantification of specific gene transcripts through reverse-transcription qPCR has been shown to enable predictions on the rates of associated microbial community functions.20 Work with enzyme inhibitors has allowed for determination of the relative contribution of specific enzymes to specific community functions.21 To our knowledge, no attempts have been made to systematically identify WWTP parameters that associate with the biotransformation of a diverse set of micropollutants and to subsequently characterize the relevant genetic and/or enzymatic content of the microbial community. We hypothesize that associations observed between micropollutant biotransformation rates and WWTP parameters along with subsequent characterization of relevant genetic content and/or enzymatic activity will lead to improved understanding of the underlying microbial processes involved in micropollutant biotransformation reactions in WWTPs. To test this hypothesis, we (i) selected a diverse set of ten WWTPs employing activated sludge systems; (ii) selected a diverse set of ten organic micropollutants; (iii) qualitatively and quantitatively characterized the biotransformation of these micropollutants in self-consistent experimental (batch) systems seeded with activated sludge microbial communities from the WWTPs; (iv) tested for statistical associations between micropollutant biotransformations and a variety of WWTP parameters; (v) tested for statistical associations between micropollutant biotransformations and relevant transcripts based on the associations identified above; and (vi) inhibited the relevant enzymes to experimentally test whether the identified associations reflect causality.

Table 1. List of Compounds, Structures, Biotransformation Reactions, and Chemical Suppliers

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EXPERIMENTAL PROCEDURES Compound Selection and Mixture Preparation. Ten environmentally relevant micropollutants (including pharmaceuticals and pesticides) were selected for this work. Selection criteria included previous elucidation of biotransformation pathways catalyzed by environmentally derived microorganisms and the availability of authentic standards for the known environmentally occurring biotransformation product(s). Four of the compounds were pharmaceuticals that have been previously identified in WWTP influents22,23 and six of the compounds were pesticides that could enter (and have been measured in) WWTPs through runoff from agriculture and combined sewers.24−27 The compounds were additionally selected to represent a diverse set of initial biotransformation pathways to enable testing for associations between biotransformation reaction rates and WWTP parameters as a function of specific biotransformation pathways. Selecting compounds presumed to undergo diverse biotransformation reactions further facilitated experiments in mixtures by limiting enzyme competition and apparent inhibition of biotransformation rates. The micropollutants, reaction types, transformation products, and suppliers are provided in Table 1. Whereas the individual micropollutants were selected to represent a diverse

1

Sigma-Aldrich (Seelze, Germany). 2Toronto Research Chemicals (North York, Canada). 3Dr. Ehrensdorfer (Augsburg, Germany). 4 ReseaChem (Burgdorf, Switzerland). 5Ultra Scientific (N. Kingstown, RI). 6Novartis (Basel, Switzerland). 7Synthesized in-house.

Wastewater Treatment Plant Selection and Sampling. Ten wastewater treatment plants (WWTPs) were selected for this work. WWTPs were selected to have a broad range of influent chemical compositions (e.g., domestic versus industrial WWTPs), operating conditions (e.g., nitrification, conventional activated sludge, membrane bioreactor), and solids retention times. The relevant WWTP characteristics are detailed in Table 2. One liter activated sludge samples were collected directly from the biological aeration basin of each WWTP and placed in 2 L amber glass bottles. Bottles were loosely capped during transport back to the laboratory and periodically shaken to maintain aeration. Once in the lab, magnetic stir bars were added to the 2 L bottles and samples were stirred at a rate that produced a vortex on the sample surface. All biotransformation experiments were started within three hours of sampling. 10580

dx.doi.org/10.1021/es3019012 | Environ. Sci. Technol. 2012, 46, 10579−10588


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Table 2. List of WWTPs and Relevant Selection Criteriaa WWTP ID DOM1 DOM2 DOM3 DOM4 DOM5 IND1 IND2 IND3 IND4 IND5

influent source municipal + psychiatric facilityb municipal municipal municipal municipal pigments, dyes, UV absorbers + minor municipal influent vitamin production paper production pharmaceuticals, chemicals, textile dyes various chemical industries

solids retention time, [days]

total suspended solids, [g/L]

A+E

5.1

3.0

A B+E+F A A+E+F A+E+F

5.4 25 2.6 20.5 9.5

3.0 12.4 2.3 2.2 6.8

C+E+F D A

12 6.1 99.6

4.7 6.9 6.0

5

9.6

processesc

A

121 °C and 103 kPa for 20 min and 24 h apart as previously described.31 Total suspended solids concentrations were measured in triplicate using Standard Method 2540B.32 The standard solutions were used to prepare an external, matrixmatched calibration series ranging between 0 μg/L and 200 μg/ L. Mass Spectrometry. The aqueous phase concentrations of each parent compound and transformation product were quantified by means of linear ion trap-orbitrap mass spectrometry (Orbitrap, Thermo, Waltham, MA) as previously detailed.30 All parent compounds and transformation products were identified with authentic standards and quantification was facilitated with a matrix-matched external calibration. Estimation of Specific Biotransformation Rate Constants. Specific biotransformation rate constants for each of the biotransformation reactions shown in Table 1 were estimated from the batch concentration time series assuming first-order transformation kinetics as shown in eqs 1 and 2:

a

dC T,PC

Data is compiled from WWTP operators and a report on the standardization of Swiss WWTPs (Def inition und Standardisierung von Kennzahlen f ür die Abwasserentsorgung) dated September 2006. b Psychiatric facility contains 350 beds and approximately 250 employees. cProcesses defined as A − conventional activated sludge (CAS); B − membrane bioreactor; C − biotower + CASd; D − moving bed bioreactor + CASd; E − nitrification; F − denitrification. d Sludge grab samples from CAS process of biological treatment.

dt

dC T,TP dt

= −k bio,PCCaq,PC

(1)

= θk bio,PCCaq,PC − k bio,TPCaq,TP

(2)

where, CT,PC and CT,TP are the total concentrations of the parent compound and transformation product, respectively, [mol/L]; kbio,PC and kbio,TP are the first-order biotransformation rate constants of the parent compound and transformation product, respectively, [day−1]; Caq,PC and Caq,TP are the aqueous phase concentrations of the parent compound and transformation product, respectively, [mol/L]; and θ is the fraction of formation of the specific biotransformation product TP. The total concentration of each species is defined as the sum of the aqueous phase concentration (Caq) and the solid phase concentration (Cs), written as

Biotransformation Assays. Batch systems were used to provide a self-consistent experimental system in which physicochemical conditions can be measured and controlled. Whereas limitations may arise in using batch reactors to simulate biotransformation kinetics in full-scale WWTPs, batch reactors have previously been shown to simulate biotransformation reactions22,28 and kinetics as observed in full-scale WWTPs reasonably well.22,29 The biotransformation experiments were carried out in 100 mL amber glass Schott bottles (bioreactors) as previously detailed.30 To minimize the potential effects of the methanol solvent, 70 μL of the spike mixture of micropollutants was added to each bioreactor prior to addition of sampled activated sludge. Methanol was allowed to evaporate in a fume hood with gentle air circulation for approximately 30 min, leaving seven μg of each micropollutant plated on the reactor glass. Seventy milliliters of activated sludge were added to each bioreactor resulting in initial concentrations of each micropollutant of 100 μg/L (background concentrations in the activated sludge were generally negligible); reactors were placed on a shaker table for at least two minutes prior to taking the first sample to allow for mixing and complete dissolution of micropollutants. Bioreactors were loosely capped and placed on a shaker table in a temperature controlled room (20 °C) to ensure continuous mixing and aeration. Dissolved oxygen concentrations were measured throughout the experiments and remained near saturation. All biotransformation assays were run in triplicate. Two mL samples were collected in 10 mL borosilicate glass syringes (Macherey-Nagel, Düren, Germany) and filtered through 25 mm diameter, 0.7 μm glass fiber syringe filters (Arcodisc, East Hills, New York) directly into 1.5 mL amber glass vials. Vials were crimp sealed and stored at 4 °C in the dark until analysis. Samples were collected at time points t = 0 (triplicate samples), 4 h, 8 h, 24 h (triplicate samples), 48 h, and 96 h to enable robust estimation of the biotransformation rate. Control reactors were prepared by twice autoclaving bioreactors at

C T,PC = Cs ,PC + Caq,PC and CT ,TP = Cs ,TP + Caq ,TP

(3)

and is related by the solids partitioning coefficient (Kd) defined for both the parent compound and the transformation product, respectively as Kd,PC =

Cs,PC XssCaq,PC

and Kd,TP =

Cs,TP XssCaq,TP

(4)

where Xss is the total suspended solids concentration of the WWTP, [gss/L]. Using these relationships, we can derive differential equations for the aqueous phase concentrations of the parent compound and transformation product as dCaq,PC dt dCaq,TP dt

=

=

−k bio,PCCaq,PC 1 + Kd,PCXss

(5)

θk bio,PCCaq,PC − k bio,TPCaq,TP 1 + Kd,TPXss

(6)

The values for Caq,PC and Caq,TP were directly measured for each sample from the bioreactor. Assuming instantaneous sorption, Kd,PC can be related to the known spike concentration (C0,spike) and the measured aqueous phase concentration of the parent compound at time zero (C0,aq,PC): 10581



Kd,PC =

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⎛ C0,spike ⎞ ⎜ − 1⎟ ⎝ C0,aq,PC ⎠ Xss

(Promega), 2 μL of the reverse-transcribed total RNA, and 0.2 μM each of the archaeal-specific Arch-amoAF and ArchamoAR primers34 or the bacterial-specific amoA-1F* and amoA-2R primers.35,36 The same thermal cycling conditions were used for both primer sets and were as follows: 5 min at 95 °C; 40 cycles of 45 s at 94 °C, 60 s at 53 °C, and 60 s at 72 °C; 10 min at 72 °C. To characterize the rank abundances of archaeal and bacterial amoA transcripts among the ten different activated sludge communities, a single fluorescence value (R) was identified that corresponded with exponential amplification of archaeal or bacterial amoA cDNA in every sample. The threshold cycle number [CT(R)] (the average of three triplicate analyses) at that single fluorescence value (R) was used to rank the abundance levels of archaeal or bacterial amoA cDNAs among the different communities, where lower CT(R) values indicate higher rank abundances (see results in Table S2 of the SI). Inhibition Assay. The inhibition assay was modified from one previously reported.21 Briefly, 50 mL aliquots of activated sludge from one of the domestic WWTPs (DOM5) were added to twelve 100 mL serum bottles. Four experiments were each run in triplicate. Following six hours of shaking, three bottles were spiked with ammonia to a starting concentration of 10 mg-N/L and three bottles were spiked with the ten micropollutants to starting concentrations of 100 μg/L each as described above. The remaining six bottles were capped with rubber septa and specific monooxygenase activity was inhibited by adding 100 μL of acetylene at 1 atm with a gastight glass syringe. After 6 h of incubation with the added acetylene, either ammonia or the 10 micropollutants were added to the bottles with a glass syringe to starting concentrations of 10 mg-N/L and 100 μg/L each, respectively. Samples were taken immediately following spiking of ammonia or micropollutants and at 24 h intervals over five days thereafter. Ammonia concentrations were measured by a colorimetric test (Hach Lange, LCK 305) and spectrophotometry (Hach Lange, DR 2800) and micropollutant concentrations were measured by means of linear ion trap-orbitrap mass spectrometry as described above.

(7)

We also defined a value r that represents the ratio between the solids partitioning coefficient of the parent compound and the transformation product.

r=

Kd,TP Kd,PC

(8)

Using eqs 7 and 8, the differential equations representing the aqueous phase concentrations can be written as follows: dCaq,PC dt dCaq,TP dt

=

=

−k bio,PCC0,aq,PCCaq,PC C0,spike

(9)

θk bio,PCCaq,PC − k bio,TPCaq,TP ⎛ C0,spike ⎞ 1 + r⎜ C − 1⎟ ⎝ 0,aq,PC ⎠

(10)

Equations 9 and 10 were used to infer the overall biotransformation rate constant of the parent compound along with the other model parameters by means of Bayesian parameter inference and Markov Chain Monte Carlo sampling. Details on the parameter estimation method can be found in the Supporting Information (SI) in the section titled “Bayesian parameter inference.” Extraction and Purification of Total RNA. Total RNA was extracted using a conventional acid phenol protocol to lyse the cells and isolate total RNA from 2 mL culture samples as previously described.20 The performance of this protocol has been previously described for activated sludge samples.33 Microbiological samples were withdrawn for total RNA extraction and purification from one of the triplicate bioreactors at time point 2 h of the biotransformation experiment. This time point was selected because no lag phase was observed in the biotransformation of any of the micropollutants, and in the majority of cases most had only been partially degraded at this time point. Residual DNA was digested with the commercially available TURBO DNA-free kit (Applied Biosystems). Quality control for residual DNA digestion was performed by PCR amplification of community 16S rDNA using the bacterialspecific 27F and 1492R primers. DNA digestion was repeated until the representative 16S rDNA bands were no longer visible by gel electrophoresis. Total RNA was purified and concentrated with the commercially available RNeasy MinElute Cleanup columns (Qiagen). Total RNA purity (A260/A280 and A260/A230) and mass concentrations were measured with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies). All total RNA samples had A260/A280 and A260/A230 values greater than 1.85. A summary of extracted masses following each extraction and purification step is provided in Table S2 of the SI. Reverse Transcription-Quantitative PCR (RT-qPCR) Analysis of amoA Transcripts. cDNA was synthesized from purified total RNA (2 μL) using the SuperScript III First Strand Synthesis SuperMix (Invitrogen) and 0.1 μM of either the archaeal-specific Arch-amoAR reverse primer34 or the bacterial-specific amoA-2R reverse primer.35 qPCR reactions were performed using a 7500 Fast Real-Time PCR System (Applied Biosystems). Each 20 μL reaction contained 10 μL of the GoTaq qPCR Master Mix (contains SYBR Green)

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RESULTS AND DISCUSSION Estimation of Biotransformation Rate Constants. Parameter estimation yielded a set of posterior marginal distributions for all parameters in eqs 9 and 10. These allowed for estimation of a maximum likelihood value for each parameter along with given quantiles. In the following presentation of results, maximum likelihood estimates of the first-order biotransformation rate constants of the parent compound (kbio,PC) are provided. A typical example of a measured concentration time series versus model fit along with r2 values, biodegradation rate constants, and solids partitioning coefficients for all time series are provided in the SI. For the majority of experiments, the expected transformation products given in Table 1 were identified with generally high but variable fractions of formation. See the SI for discussions on fractions of formation of the expected transformation products. No abiotic transformation processes were observed in the control experiments. Biotransformation of Individual Micropollutants. The first-order biotransformation rate constants for each micropollutant as estimated for each of the ten activated sludge communities are summarized in box plots in Figure 1. The data show how compound-specific properties are important in 10582



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poorest for 6 of the 10 micropollutants, no clear pattern emerged among the other WWTPs, as they are ordered differently for every micropollutant. In fact, the generally poorly performing IND5 catalyzed biotransformation reactions with diazinon faster than any other WWTP. These results further demonstrate how different activated sludge communities can have very different micropollutant biotransformation capabilities, even in terms of specific biotransformation reactions at the individual micropollutant level. To test for statistical associations between overall micropollutant biotransformation performance and each of the WWTP parameters, we ranked the WWTPs in terms of their overall biotransformation performance (see the SI for details). In short, each WWTP was assigned a ranking from 1 (largest biotransformation rate constant) to 10 (smallest biotransformation rate constant) for each individual micropollutant, based on the maximum likelihood estimates of their biotransformation rate constant (i.e., their position in Figure 2 for each compound). WWTP ranks for each individual micropollutant were then summed and WWTPs were reranked based on this cumulative score (see the first two rows of Table 3 for cumulative scores and ranking in terms of overall micropollutant biotransformation performance). From this data, we can conclude that DOM3 and DOM1 are the best performing WWTPs in terms of overall micropollutant biotransformation and IND4, IND5, and DOM4 are worst performing. It is also interesting to note that there is no clear clustering of domestic and industrial WWTPs in terms of overall micropollutant biotransformation performance. Statistical Associations with WWTP Operational Parameters. In Table 3, WWTP parameters that are presumed to influence overall micropollutant biotransformation performance are provided (as recorded on the day of activated sludge sampling). The parameters given in Table 3 were selected to represent a variety of physicochemical and operational conditions that are unique to each WWTP. We first used Spearman’s rank correlation to test for statistical associations between overall micropollutant biotransformation performance (the top row of Table 3) and each of the WWTP parameters; Spearman’s rho and p-values of the rank correlation test are provided in the right-most columns of Table 3 (see the SI for discussion on effects of looking at two types (domestic and industrial) of WWTP communities). The majority of the parameters did not show any significant association with overall micropollutant biotransformation performance, most notably TSS (p-value of 0.37) and SRT (p-value of 0.60) which demonstrates that neither biomass concentrations nor sludge age alone can explain the variability

driving kinetics of biotransformation reactions; the mean values for micropollutants whose structures are amenable to substitution reactions are generally higher than for those micropollutants whose structures are amenable to oxidation reactions (with the exception of ranitidine, which was biotransformed by N- or S-oxidation reactions, as opposed to dealkylation reactions). This result is supported by previous work investigating the preferred biotransformation pathway of amide-containing compounds.31 Despite this observation, the biotransformation rate constants of each micropollutant ranged between 1 and 4 orders of magnitude among the WWTPs, suggesting that the biomass from each WWTP is unique and has a different set of micropollutant biotransformation capabilities. A likely hypothesis is that the unique metabolic capabilities of each community are shaped by the unique WWTP parameters that ultimately control the transcript and enzyme pools.

Figure 1. Box plot comparing the maximum likelihood estimates of the first-order biotransformation rate constants for each of the 10 micropollutants.

Biotransformation Trends Among WWTPs. The observed variability in the estimated biotransformation rate constants was not simply the result of consistently wellperforming or poorly performing WWTPs. In Figure 2, the ranking of each WWTP in terms of the maximum likelihood estimate of the biotransformation rate constant for each micropollutant is presented. Although DOM3 performed the best for 6 of the 10 micropollutants and IND5 performed the

Figure 2. Ranking of the maximum likelihood estimates of the parent micropollutant biotransformation rate constants in each of the ten WWTPs. 10583



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Table 3. Operational Parameters from Each WWTP on Day of Sampling DOM1

DOM2

DOM3

DOM4

DOM5

IND1

IND2

IND3

IND4

IND5

rho

p-valuea

overall micropollutant removal ranking cumulative score

2

5

1

10

7

6

4

3

8

9

31

52

15

87

65

55

47

41

78

79

WW temp, [°C] WW pH WW diss. O2, [mg/L]

9 7.6 1.7

12 7.6 1.7

17 7.5 0.8

11 8.1 6.2

8 8.1 2.4

25 7.0 2.8

29 7.3 1.5

35 6.2 4.8

24 7.0 3.4

41 8.1 1.0

0.0303 0.4445 0.4255

0.9338 0.1980 0.2202

c

BOD5 in, [mg/L] BOD5 out, [mg/L] Δ BOD5, [mg/L] BOD5 rem (%) c COD in, [mg/L] COD out, [mg/L] Δ COD, [mg/L] COD rem (%) c NH4−N in, [mg/L] NH4−N, out, [mg/L] Δ NH4−N,[mg/L] NH4−N rem (%) c Ptot in, [mg/L] Ptot out, [mg/L] Δ Ptot, [mg/L] Ptot rem (%)

203 8 195 95.9 310 34 276 88.9 18.6 0.1 18.5 99.2 5 1.3 3.7 71.7

54 3 51 94.8 96 42 54 56.2 3.3 0.2 3.1 92.7 2 0.1 1.9 92.9

320 1 319 99.7 nab na na na 24.9 0.0 24.9 99.8 9 0.4 8.6 95.3

160 10 150 93.8 332 19 313 94.3 10 5.0 5.0 50.0 3 0.1 2.9 96.5

na na na na na na na na 26.2 0.1 26.1 99.8 5 0.1 4.9 97.1

500 na na na 846 165 681 80.5 23 0.9 22.1 96.1 3 na na na

na na na na 820 na na na 86 2.0 84.0 97.7 6 1.6 4.4 71.4

1,892 25 1,867 98.7 1,873 219 1,654 88.3 na na na na na na na na

650 8 642 98.8 1,565 208 1,357 86.7 86.2 57.2 29 33.6 na na na na

3,000 159 2,841 94.7 na na na na 100 62.0 38 38.0 22 17.6 4.4 18.5

0.1429 0.5357 0.0357 −0.7500 0.0714 −0.2077 0.1429 −0.0231 0.2167 0.7333 −0.0667 -0.6667 −0.1190 −0.2857 −0.4286 0.1429

0.7358 0.2152 0.9394 0.0522 0.8790 0.6550 0.7872 0.9608 0.5755 0.0246 0.8647 0.0499 0.7789 0.5345 0.3374 0.7599

c

3.0 1,622 4.0 5.1

3.0 17,358 2.8 5.4

12.4 962 24.0 9.8

2.3 16,375 5.9 2.6

2.2 10,314 20.0 20.5

6.8 9,119 14.0 9.5

4.7 8,113 17.0 12.0

6.9 21,360 21.3 6.1

6.0 5,720 35.7 99.6

9.6 5,800 20.0 5.0

−0.3161 0.2727 −0.0304 −0.1879

0.3736 0.4458 0.9336 0.6032

TSS, [g/L] Q, tot daily [m3] c HRT, [hour] c SRT, [day] c

a

Rank correlation is calculated between the overall micropollutant removal ranking and each WWTP-specific parameter provided in Table 3. pvalues are derived from the two-tailed F probability distribution based on Spearman’s rho. bna means that the data was not available from the WWTP. cBOD5 − biochemical oxygen demand; COD − chemical oxygen demand; NH4−N − ammonia nitrogen; Ptot − total phosphorus; TSS − total suspended solids; Q − flow rate; HRT − hydraulic retention time; SRT − solids retention time.

Figure 3. p-values of association tests between removal of ammonia and each of the ten micropollutants and (a) ammonia removal and (b) archaeal and bacterial amoA transcript abundance. p-values for associations with NH4−N removal and amoA transcripts are derived from the two-tailed F probability distribution based on Spearman’s rho.

in biotransformation of a diverse set of micropollutants in WWTPs. Only the effluent concentration and removal of NH4−N showed convincing statistical associations with overall micropollutant removal (p-values of 0.025 and 0.050, respectively) with lower effluent ammonia concentrations and higher ammonia removal efficiencies associating with enhanced

micropollutant removal. Interestingly, the total amount of nitrification (ΔNH4−N, p-value of 0.86) did not associate with overall micropollutant biotransformation performance. If ammonia oxidizing bacteria were contributing significantly to the rates of the biotransformation reactions observed among the individual micropollutants investigated, one would expect 10584



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merely an indirect association. Further insights on causality could only be obtained through additional characterization of the microbial community as described below. Statistical Associations with Archaeal and Bacterial amoA Gene Transcripts. To investigate the importance of ammonia oxidizing microorganisms for micropollutant biotransformation reactions and oxidation reactions in particular, we needed to quantify the relative abundance of metabolically active ammonia oxidizers in each WWTP. We used transcriptspecific primers and RT-qPCR to amplify both archaeal and bacterial amoA transcripts and quantified the rank abundances of archaeal and bacterial amoA transcripts among the ten different activated sludge communities. Whereas ammoniaoxidizing Bacteria (AOB) are well-known players in ammonia oxidation in WWTPs, we additionally selected archaeal primers based on the known occurrence of ammonia oxidizing Archaea (AOA) in wastewater treatment plants45−51. We detected archaeal and bacterial amoA transcripts at levels significantly greater than the negative control in all samples, confirming that amoA transcripts from both AOAs and AOBs are abundant in all the investigated WWTPs (see SI Table S2 for results). We then used Spearman’s rank correlation to test for statistical associations among the rankings of the individual micropollutant biotransformation rate constants (Table 2) and the rank abundances of amoA transcripts from AOA and AOBs. The results of these analyses are summarized in Figure 3b. Because it is presumed that microbial AMO is the sole enzyme catalyzing ammonia oxidation reactions, we first checked to ensure statistical associations were evident between the archaeal and bacterial amoA transcripts and NH4−N removal. As shown in Figure 3b, a significant association was identified between relative abundances of archaeal amoA gene transcripts and NH4−N removal (p-value of 0.016) while no association was identified between the relative abundance of bacterial amoA transcripts and NH4−N removal (p-value of 0.52). While there is again no direct evidence of causation in this analysis, the data suggests that archaeal amoA may be a relevant contributor to ammonia oxidation in WWTPs, as has been shown for soil communities.52,53 As for the micropollutants, associations were identified between the abundance of archaeal amoA transcripts and the removal of three compounds, while no associations were identified for the bacterial amoA . The three compounds for which associations were identified had all previously shown associations with NH4−N removal, strengthening the apparent link between ammonia removal and biotransformation of specific micropollutants. The three compounds that showed associations were isoproturon (p-value of 0.011), ranitidine (pvalue of 0.042), and venlafaxine (p-value of 0.016), all of which undergo oxidation reactions. As with the associations identified with NH4−N removal, the associations identified here among micropollutant biotransformations and the rank abundance of archaeal amoA transcripts suggests that there is potentially a predictive linkage between archaeal amoA transcripts and oxidative micropollutant biotransformation without any direct evidence of causation. Further, this adds support to the hypothesis that ammonia oxidizers are contributing significantly to oxidative micropollutant biotransformation reactions, though this contribution seems to arise from archaeal as opposed to bacterial ammonia oxidizers. Inhibition of Monooxygenases. To confirm or reject direct causation of the observed associations, we sought to

stronger associations with the total amount of nitrification, which along with parameters such as HRT and SRT controls the abundance of ammonia oxidizing microorganisms in a WWTP.37 Nevertheless, given that the only convincing statistical associations observed were with the effluent concentration and removal of NH4−N, the underlying microbial process still seems to be linked to ammonia removal. Other mechanistic hypotheses could support these associations. For example, ammonia monooxygenase may have a significantly stronger affinity for ammonia relative to micropollutants and may preferentially oxidize ammonia when ammonia concentrations remain high. Likewise, substrate limiting conditions may lead to a higher cometabolic activity of ammonia monooxygenase. Additionally, the connection to ammonia oxidizing micoorganisms is not completely unexpected as there is much literature demonstrating enhanced removal of organic micropollutants in nitrifying activated sludge systems.13,14,16−18,38,39 In particular, AMO from the bacterium Nitrosomonas europaea has been shown to catalyze oxidation reactions with estrogens and other aromatic compounds.13−15,40,41 Although a positive association with NH4−N effluent concentrations and removal was identified, the WWTPs with the highest removal of NH4−N were not the most efficient WWTPs at removing all of the individual micropollutants, as discussed relative to Figure 2. To further investigate whether NH4−N removal associates equally well among the 10 micropollutants, correlation analyses were again performed at the individual micropollutant level. Here, we used the rankings of the maximum likelihood estimates of the biotransformation rate constant for each compound in each WWTP as given in Figure 2. The results of this analysis are presented in Figure 3a. We found that only five of the compounds studied showed significant associations with ammonia removal, namely: atenolol (p-value of 0.042); isoproturon (p-value of 0.0025); ranitidine (p-value of 0.0016); valsartan (p-value of 0.012); and venlafaxine (p-value of 0.0072). These compounds represent one of the five substitution reactions (a primary amide hydrolysis reaction for atenolol), and four of the five oxidation reactions (the S- or N-oxidation reaction for ranitidine and all three oxidative N-dealkylation reactions). It is interesting that the majority of the compounds that undergo oxidation reactions (in particular all of the N-dealkylation reactions) show associations with ammonia removal while the majority of the substitution reactions (with the exception of atenolol) do not. It has been shown previously that when AMO is playing a significant role in micropollutant removal (along with ammonia removal), the reactions catalyzed are likely to be hydroxylations.13,18 Hydroxylation at the α-C has also been shown to be the initial transformation step in N-dealkylation reactions42,43. Also of note is that ethofumesate, which primarily undergoes an oxidative O-dealkylation reaction does not associate with NH4−N removal. It is not mechanistically clear why oxidative O-dealkylation would not associate since oxidative dealkylation of alkyl−alkyl and aryl−alkyl ethers is catalyzed by a wide variety of oxidative enzymes, presumably including monooxygenases. 44 The preceding analysis shows that overall micropollutant removal, and oxidative biotransformations in particular, associate with NH4−N effluent concentrations and removal percentage. This result indicates that there is potentially a predictive linkage between these two community functions. However, it remains unclear whether this linkage is causal or 10585



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catalyzed by ammonia monooxygenases. Further, our results suggest that future research is warranted to investigate the relative contribution of ammonia oxidizing Archaea to both ammonia removal and micropollutant removal in WWTPs. Future work should also be directed toward full scale WWTP investigations to validate results reported from lab-based studies.

measure the relative contribution of monooxygenases (and ammonia monooxygenase in particular) to oxidative micropollutant biotransformation reactions. We compared micropollutant biotransformation rate constants for each of the ten micropollutants and ammonia oxidation when in the presence or absence of acetylene, which is known to inhibit ammonia monooxygenase along with a variety of other monooxygenases. 21 We performed this comparison for the community derived from DOM5. As expected, ammonia removal was inhibited with the use of acetylene (p-value of 0.0085). Unexpectedly, however, the biotransformations of only one micropollutant, isoproturon, was inhibited by acetylene (p-value of 0.016, see SI for more details). Isoproturon is a compound that undergoes a tertiary urea demethylation reaction and is the only compound whose biotransformation rates correlated positively with NH4−N effluent concentrations and removal, archaeal amoA transcripts, and acetylene-inhibited monooxygenase activity. We can therefore conclude that either ammonia monooxygenase or another acetylene-inhibited monooxygenase significantly contributes to the biotransformation of this compound (and perhaps of this compound class). The data further suggests that the reaction may be catalyzed by monooxygenases from archaeal ammonia oxidizers, which to our knowledge is the first such association observed between archaea and micropollutant biotransformation. Given the strong and positive associations observed between oxidative micropollutant biotransformations and both ammonia removal and expressed archaeal amoA content, it is surprising that inhibition of ammonia monooxygenase activity had little to no effect on the remaining micropollutants that undergo oxidative biotransformations. This result demonstrates that, while ammonia removal and archaeal amoA transcript abundance could potentially serve as general predictors of oxidative micropollutant removal, the underlying biochemical process is not necessarily controlled by the expected enzymes, an observation that was only enabled through deeper investigations into relevant genetic content and/or enzymatic activity. It is possible that other enzymatic systems in archaeal ammonia oxidizers are significantly contributing to the observed micropollutant biotransformation reactions, or that the presence of archaeal ammonia oxidizers is indicative of other fundamental underlying autotrophic or heterotrophic microbial processes. For example, ammonia oxidation by AMO to hydroxylamine is only the first step in the biological nitrification process; enzymes that catalyze the subsequent reactions of hydroxylamine to nitrite (hydroxylamine oxidoreductase) or nitrite to nitrate (nitrite oxidoreductase) could be significantly contributing to the observed oxidative biotransformation reactions. In this case, the statistical associations reported at the functional (NH4−N removal) and transcript (archaeal amoA abundance) level would remain as potential predictors of oxidative micropollutant biotransformation. However, developing a method to test for the relative contribution of these oxidoreductases was outside the scope of this work. Our results contribute to the general understanding of the WWTP parameters influencing micropollutant removal. We have shown that WWTPs that have nearly complete biological ammonia removal have significantly higher biotransformation rates of micropollutants whose primary transformation is through oxidation reactions such as N-dealkylation or S- or N-oxidation, but that these reactions are not necessarily

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ASSOCIATED CONTENT

S Supporting Information *

Recoveries for each compound in each matrix; details on Bayesian parameter inference; masses of nucleic acid material extracted; typical model output and goodness-of-fit metric; discussion of RT-qPCR analysis; estimated first-order biotransformation rate constants; estimated solids partitioning coefficients; details on the fraction of formation of specific transformation products; discussion of normalization of data; discussion on ranking of WWTP performance; statistical analysis for pseudoreplication; and details on the inhibition assay and results. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +41 58 765 55 03; fax: +41 58 765 53 11; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the operators and staff at each of the anonymous wastewater treatment plants for providing access to their biological processes and aggregating relevant data for our study. We additionally thank Drs. Jessica Benner, Nico Boon, HansPeter Kohler, and George Wells for very fruitful discussions. Finally, we thank Dr. Rolf Kuemmerli, Dr. Markus Kalisch, and Anna Drewek for statistical support. This work was funded internally by Eawag Discretionary Funds for Research− Category: Seed Projects.

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REFERENCES

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Micropollutant Biotransformation Kinetics Associate with WWTP

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