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Chemical Characterization of Low Molecular Weight (MW < 580 Da) Soluble Microbial Products (SMPs) in an Anaerobic Membrane Bioreactor Chinagarn Kunacheva, Chencheng Le, Yan Ni Annie Soh, and David C Stuckey Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05791 • Publication Date (Web): 26 Jan 2017 Downloaded from http://pubs.acs.org on January 30, 2017

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Environmental Science & Technology

1

Chemical Characterization of Low Molecular Weight (MW < 580 Da)

2

Soluble Microbial Products (SMPs) in an Anaerobic Membrane Bioreactor

3 4

Chinagarn Kunachevaa, Chencheng Lea,b, Yan Ni Annie Soha and David C. Stuckeya,c*

5 6

a

7

Institute, Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore

8

637141, Singapore.

9

b

Advanced Environmental Biotechnology Center, Nanyang Environment & Water Research

Division of Environmental and Water Resources Engineering, School of Civil and

10

Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue,

11

Singapore 639798, Singapore.

12

c

Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK

13 14

*Corresponding author

15

E-mail: [email protected], Tel: +44 207 5945591

16 17

Abstract

18

Effluents from wastewater treatment systems contain a variety of organic compounds,

19

including end products from the degradation of influent substrates, non-biodegradable feed

20

compounds, and soluble microbial products (SMPs) produced by microbial metabolism. It is

21

important to identify the major components of these SMPs in order to understand what is in

22

wastewater effluents. In this study, physical pretreatments to extract and concentrate low

23

molecular weight SMPs (MW< 580 Da) from effluents were optimised. Liquid-liquid

24

extraction (LLE) of a 200 mL effluent sample showed the best performance using a mixture

25

of n-hexane, chloroform and dichloromethane (70 mL) for extraction. For solid phase

1 Environment ACS Paragon Plus


26

extraction (SPE), two OasisHLB cartridges were connected in-line to optimise recovery, and

27

the eluted samples from each cartridge were analysed separately to avoid overlapping peaks.

28

Four solvents varying from polar through to non-polar (methanol, acetone, dichloromethane,

29

n-hexane) were selected to maximise the number of compound peaks eluted. A combination

30

of SPE (OasisHLB) followed by LLE was shown to maximise compound identification and

31

quantification. However, the compounds identified only accounted for 2.1 mg COD/L (16%

32

of total SMP as COD) because many SMPs have considerably higher MWs. Finally, the

33

method was validated by analysing a variety of different reactor effluents and feeds.

34 35

Graphical Abstract Effluent

Analysis GC-MS m/z = 30-580

5μm

Size

1μm 100k

SMP and ECP

Solid Phase Extrac on

Liquid-Liquid Extrac on

10k OH

MW < 580 O

OH

Elu on MeoH Acetone DCM Hexane

Extrac on Hexane + DCM + Chloroform

36 37 38

Key words: anaerobic digestion, membranes, characterization, dissolved organic matter,

39

soluble microbial products (SMP), GC-MS

40 41

1. Introduction

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Biological wastewater treatment (both aerobic and anaerobic) has been the process of choice

43

for many years, although these systems are never 100% efficient, and hence significant

44

amounts of organic soluble microbial products (SMPs) are present in the effluent defining the

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limits to treatment. These effluents contain a variety of complex organic compounds,

46

including transformation products from the (partial) degradation of influent substrates, non-


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biodegradable compounds in the feed, and SMPs including extracellular polymer (ECPs),

48

which are produced by microorganisms during metabolism and cell lysis 1. Many effluent

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SMPs are degradable over time in both aerobic and anaerobic processes, although the

50

hydraulic retention time in these processes often limits complete degradation 2. Hence

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minimizing SMP production can increase treatment performance

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by-products 4, and reduce fouling in membrane bioreactors 5. However, to achieve these goals

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it is important to characterise the chemical composition of SMPs in order to begin to

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understand the metabolic pathways that create these compounds, why they do not degrade,

55

and how to reduce them in the effluent.

1, 3

, minimise chlorination

56 57

There are a limited number of papers in the literature analysing SMP composition in

58

biological systems, and only a few researchers have used advanced instruments such as gas

59

chromatography-mass spectrometry (GC-MS)

60

trap time-of-flight mass spectrometry (LC-IT-TOF-MS)

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desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS)

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surprisingly found long chain alkanes, alkenes, and aromatic carbons such as bis(2-ethylhexyl

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phthalates in significant concentrations in a reactor fed with only synthetic wastewater (sugar,

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nutrients, and minerals). Similar compounds were also found in the effluent of a submerged

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anaerobic membrane bioreactor (SAMBR) treating municipal solid waste leachate

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additionally, esters, phenols and amides were also found in the effluent in significant

67

concentrations. Another study in a full-scale upflow anaerobic sludge blanket (UASB)

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reactor revealed long chain carbohydrates, and esters 8, and these compounds were not only

69

found in an anaerobic processes, but also in an aerobic sequencing batch reactor (SBR).

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Currently SMPs are analysed using a wide range of different pretreatments and analytical

71

methodologies, and hence the data produced is often difficult to compare, and the maximum

6-8

, high-resolution liquid chromatography ion 9


, and matrix-assisted laser 7, 9

. Aquino

7

6

;


72

number of compounds identified is only 27 7. A detailed analytical methodology for SMPs in

73

wastewater needs to be developed so that the data obtained by different researchers can be

74

compared. Hence, the objective of this study was to optimize pretreatment methods (liquid-

75

liquid extraction (LL) and solid phase extraction (SPE)) to maximise the extraction of solutes

76

from the effluent sample, and to develop a methodology for quantification of low molecular

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weight compounds (MW < 580) in SMPs. This MW limit is set by GC-MS since the

78

compounds identified need to be relatively volatile, while we have used LC-TOF-MS to

79

analyse higher MW (

228

than 80% (most matches were considerably higher). Similarity index, mass spectrum and

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retention index were all used as selection criteria for identification, and compounds with a

230

match below 80% were denoted as “unknown peaks”. Possible contamination during

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extraction was also investigated by soaking pipette tips, gloves, tubes, and septa in the

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solvents for an hour, and then measuring this extract using GC-MS (Figure S2).

, and based on the alkane standards (C10 – C40). The MS was operated in electron

233 234

2.7 Estimation of SMPs concentration

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Quantifying all the SMPs identified would be very time consuming based on individual

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compound calibrations, and hence a crude approximation was made to estimate the

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concentration of each SMP using alkanes as representative standards. The standard solution

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contains different alkane chain lengths (C10 – C40) which covered most of the volatility

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range of the RTX-5MS column, and retention indices were used for qualitative analysis

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(retention index in GC-MS analysis was calculated based on straight-chain alkanes). The

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calibration curves consisted of five concentrations (0.1, 0.25, 0.5, 1 and 2 mg/L), and the

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correlation (R2) values were greater than 0.99 except for C38 and C40 (Table S1).

243


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Alkane standards quantified the unknown compounds based on their retention time, and this

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was done separately for each unknown using the closest alkane standard which was run in

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every batch of analyses. Blank solvents (methanol, acetone, dichloromethane, n-hexane) were

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also run in every batch, and the instrument identification limit (IDL) of the alkane standards

248

was evaluated for each compound based on the maximum blank concentration, and a signal-

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to-noise ratio of 3.

250 251

3. Results and discussion

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3.1 SMP production in the SAMBR

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The SAMBR was fed with the low strength synthetic wastewater (492±18 mg COD/L) under

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steady state conditions (HRT = 6 h) for 30 days. The composition and concentration of SMPs

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and COD were monitored over time to ensure that “steady state” had been reached in SMP

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production, and hence the data reported here is the stable composition of the SMPs after HRT

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changes. The SAMBR performance was excellent, with an effluent SCOD as low as 13±1

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mg/L, and 98% COD removal; VFAs were not found in any samples in this experiment.

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100% of the SCOD was accounted for by SMPs in the effluent. Biogas contained an average

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of 74% methane, 11% carbon dioxide, and 15% nitrogen from soluble nitrogen gas in the

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feed, with no oxygen detected. Biogas production averaged 2.77 L/day, accounting for 323

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mL CH4/g.COD (82% of the theoretical value at 35 °C and 1 bar -the balance being biomass

263

growth).

264 265

3.2 Optimization of liquid-liquid extraction (LLE) for SMP analysis

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The development of this technique required the selection of suitable solvents. Although water

267

is highly polar and immiscible with most organic solvents, polar solvents like the alcohols,

268

acetone and acids do not phase separate well from water using LLE techniques because of



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their high solubility. From 30 common solvents, eight inexpensive and easily accessible ones

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were chosen, and all exhibited good solubility for a range of organic compounds.

271 272

Initially, a series of sample extracts with different solvents was evaluated, and the average

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peaks and their standard deviation (SD) were obtained from a quadruplicate analysis (n = 4).

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The results shown in Table 1 reveal that n-hexane, chloroform and dichloromethane all

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showed good extraction capability, while no peaks were found using n-heptane and toluene.

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Other solvents like diethyl ether, methyl tert-butyl ether and ethyl acetate had a high degree

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of miscibility with water resulting in poor extraction.

278 279

Table 1. Efficiency of different organic solvents evaluated for extraction. Sample volume (mL)

Solvents (ratio)

Extraction solvent volume (mL)

Total number of peaks (Mean ± SD)

n-Hexane

196 ± 2.1

n-Heptane

0

Toluene

0

Chloroform

215 ± 2.8

Diethyl ether

0

Methyl tert-butyl ether

200

Dichloromethane

20

0 232 ± 2.4

Ethyl acetate

0

n-Hexane + Chloroform (1:1)

0

n-Hexane + Dichloromethane (1:1)

0

Chloroform + Dichloromethane (1:1)

260 ± 3.4

n-Hexane + Chloroform + Dichloromethane (1:1:1)

274 ± 1.3

Note: SD = standard deviation

280 281

After GC-MS analysis, it was interesting to observe that 146 identical organic compounds

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could be identified using either n-hexane, chloroform or dichloromethane as an extractant.

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Some of the compounds detected such as alkanes, alkenes, fatty acids, and phthalates (in

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particular dibutyl phthalate and bis(2-ethylhexyl)phthalate) have also been reported

285

previously

7, 8, 15, 18

. There were 13, 33, and 11 organic compounds that could only be


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extracted independently by n-hexane, chloroform and dichloromethane, respectively, and this

287

led to investigating solvent extraction using a mixture of solvents. The average number of

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compounds extracted was higher when a mixture of chloroform and dichloromethane, as well

289

as a mixture of these solvents plus n-hexane, was used compared to individual solvents. In

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contrast, mixtures of n-hexane and chloroform, and n-hexane and dichloromethane, were

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very ineffective extracting solutes because both these mixtures had a high miscibility in water,

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and thus no solutes were extracted. Therefore, a mixture of n-hexane, chloroform and

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dichloromethane (ratio 1:1:1) was selected as the optimal extraction solvent for further study.

294 295

Secondly, since the solvents chosen were flammable and potentially toxic, the optimal

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solvent volume should be kept to a minimum. Different volumes (20 to 100 mL) of the

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optima solvent mixture were used to extract the same volume of effluent, with all other

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experimental conditions fixed. Figure S3 (Supporting Information) shows that the extraction

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efficiency was directly affected by the extracting solvent volume; both the number of peaks

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and total peak area obtained increased with an increase in the extraction volume up to 70 mL.

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However, a plateau in both was observed after the solvent volume exceeded 80 mL, although

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this decrease in peak number was not statistically significant. Hence, 70 mL of solvent

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mixture was chosen as the optimum volume for subsequent experiments.

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Thirdly, pH is often important in the extraction of organics from water samples because it can

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change the molecular form of the organic influencing its charge/hydrophobicity, and thus

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affecting extraction. “Salting out” with ionic salts is one method commonly used to enhance

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LLE performance by decreasing the aqueous solubility of the analyte, and increasing its

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partition coefficient

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extraction in acidic (pH < 2) and alkaline (pH >11) solutions, and the salt effect by adding

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NaCl and MgSO4 separately (Table S2). The results demonstrate that both solution

19

. In this work, the effect of pH was determined by conducting the



311

modifications result in no obvious improvement compared to pH = 7 with no salt addition,

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and may even decrease the efficiency. Hence, the following experiments were carried out at

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pH = 7 with no additives.

314 315

Fourthly, the influence of sample size on extraction performance was evaluated using

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different volumes of effluent, keeping all other conditions constant. When the volume of

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sample increased from 200 to 400 mL, the number of compounds detected did not increase

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significantly, although the total peak area almost doubled (Table S3). In contrast, as the

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sample volume decreased from 200 mL to 50 mL, the number of peaks and their peak area

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declined. Since the absolute mass of an analyte will decrease with a reduction in effluent

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sample volume, some of the analytes' concentration could drop below their detectable level.

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Hence, a minimum sample volume of 200 mL was deemed necessary to maximise the

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extraction of low-molecular weight SMPs.

324 325

In summary, a 70 mL mixture of n-hexane, chloroform and dichloromethane was optimal at

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pH = 7, with no addition of salts, to extract a minimum sample volume of 200 mL (~ 2.6 mg

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COD), and these conditions could detect the most low MW SMPs.

328 329

3.3 Optimization of solid phase extraction (SPE) for SMP analysis

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A. Comparison of OasisHLB and ENVI-Carb cartridge

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Two cartridges, OasisHLB and ENVI-Carb, were tested to compare their performance on

332

capturing the compounds in effluent. Effluent from the SAMBR was collected and loaded

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onto the cartridges on the same day. The compounds identified were categorized as alkanes,

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alkenes, esters, alcohols, nitrogenated compounds (N-compounds), phenols, and others;

335

previous researchers also found similar groups of compounds in SMPs 15, 18. The reactor feed


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was also analysed to determine the compounds present, and the compounds found were then

337

subtracted from the effluent sample (Table S4). Both cartridges showed similar results in

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terms of the number of compounds they separated (OasisHLB: 73 and ENVI-Carb: 70)

339

(Figure 1), however, the compounds both cartridges captured were quite different, with only

340

about a 50% similarity. OasisHLB retained more of the higher volatility compounds than

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ENVI-Carb, with average retention indices (retention time normalized to the retention times

342

of adjacently eluting n-alkanes: lower retention index equals higher volatility) of 1820 and

343

1942, respectively. Both cartridges showed very good performance in retaining low MW

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compounds (MW 580

401

Da) which resulted in a lower baseline in the LLE chromatogram, and better resolution. The

402

second was that dichloromethane was present in the sample after LLE, which affected

403

adsorption in SPE. The last issue was sample volume; for LLESPE, five extracts (200 mL

404

each) with LLE were needed for further extraction by SPE (1 L). For SPELLE, 1L of

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sample was loaded onto the OasisHLB column, and then 200mL that had passed through the

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cartridges was extracted using LLE. Hence, SPELLE was selected as an optimised

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pretreatment for low MW SMPs. An effluent sample was collected and split into 3 samples

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(Samples 1, 2, 3) for statistical validation of method reproducibility. Table 2 shows the

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number of peaks (with minimum peak area/peak height > 3000) identified using a

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combination of SPE and LLE for extraction, and demonstrates good reproducibility across

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the three samples. The chromatograms of sample 2, blanks and alkane standards are shown in

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Figure S5, S6 and S7, while the lists of compounds identified are shown in Tables S4 and S5.

413 414

Table 2. Combination of SPE and LLE for the extraction of low MW compounds in SMPs. Number of peaks (SPE)

Number of peaks (LLE)

Identified

Unidentified

Total

Identified

Unidentified

Total

Total number of peaks

56 52 53

22 25 24

78 77 77

67 68 68

113 109 110

180 177 178

258 254 255

Sample 1 2 3

415 416

Note: SPE = Solid phase extraction, LLE = liquid-liquid extraction, Identified = mass spectral similarity > 90%, Unidentified = mass spectral similarity < 90%

417


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Table 3 compares our “Standard” method to other methods in the literature. LLE was the

419

most popular pretreatment in previous research, and Aquino

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MW SMPs. He used four solvents in LLE extraction, and 18 and 9 compounds were found in

421

a sugar feed CSTR and a SAMBR, respectively. Zhou, Wu, She, Chi and Zhang 8 used LLE

422

with dichloromethane for analysing SMPs in the effluent of UASBs treating industrial

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wastewater, and more than 27 compounds were identified. The same group of researchers

424

found less low MW SMP in a reactor fed with glucose using a similar LLE method 15. SPE

425

was also applied to separate compounds in the effluent from a SAMBR, and 4 compounds

426

were identified using the sorbent ENV+ in SPE 7. Another study used OasisHLB as a

427

pretreatment to identify compounds in the effluent of a SAMBR treating solid waste leachate,

428

and 11 compounds were identified 6. Until now there was no information on pretreatment

429

optimization in the literature; in this study pretreatment was optimized, and an average of 77

430

and 178 compounds were identified from SPE and LLE, respectively, an order of magnitude

431

more compounds identified than in past work.

7

was the first to analyse low

432 433

Table 3. Comparison of the methods used in this study and the literature.

Type of Samples

Pretreatment

Analysis

Number of compounds

Mixed liquor and effluent from CSTR, SAMBR

LLE (hexane, monochloro benzene, dichloromethane, ethyl acetate)

GC-MS (Column: SGE Phase BPX5, m/z 40 to 600)

CSTR: 18 compounds (LLE), 9 compounds (SPE)

from

Effluent SAMBR

from

Effluent from activated sludge

References

Aquino (2004)

SAMBR: 9 compounds (LLE), 4 compounds (SPE)

SPE (sorbent ENV+)

Effluent UASB

identified

LLE (dichloromethane)

GC-MS (Column: DB 5MS, m/z 33 to 500, NIST98 and WILEY Registry 7.0)

27 compounds (major)

Zhou et al. (2009)

SPE (OasisHLB), Elution solvent: 10%methanol/90%MTBE

GC-MS (Column: SGE HT5, m/z 33 to 500, NIST05 library)

11 compounds

Trzcinski and Stuckey (2009)

Lyophillized

LC-IT-TOF-MS (MW 100 to 4,000 Da)

Not mentioned

Mesquita (2010)


et

al.


Effluent UASB

from

Effluent SAMBR

from

LLE (dichloromethane)

GC-MS (NIST98 WILEY Registry 7.0)

SPE (OasisHLB), Elution solvent: methanol, acetone, dichloromethane, hexane

GC-MS (Column: RTX-5MS, m/z 33 to 580, NIST11)

LLE: mixture of n-hexane, chloroform or dichloromethane

and

13 compounds (major)

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Wu and (2010)

Zhou

SPE: 63 compounds This study LLE: 227 compounds

Note: CSTR = continuous stirred tank reactor, LC-IT-TOF-MS = High resolution liquid chromatography ion trap time-of-flight mass spectrometry, LLE = liquid-liquid extraction, SAMBR = submerged anaerobic membrane bioreactor, SPE = solid phase extraction, UASB = upflow anaerobic sludge blanket

434 435

Finally, approximate quantification was done separately for each compound using the alkane

436

with the closest retention time. A set of standards was run in and between every batch of

437

analyses to minimise instrumental error, and blank solvents (methanol, acetone,

438

dichloromethane, n-hexane) were also run for background subtraction. Combined

439

concentrations of the low MW compounds using SPE and LLE were 0.009 mg/L and 0.598

440

mg/L, respectively, and the compounds identified from both methods accounted for 2.1 mg

441

COD/L (16% of the total SMP as COD calculated from 3.46 gO2/g docosane). While

442

concentration estimation using an alkane standard may not be the most accurate way to

443

quantify SMPs, the quantification is currently the best estimate and the most practical for

444

unknown samples.

445 446

Based on the results of this study, the combination of SPELLE coupled with the GC-MS

447

was shown to be “optimum” for separating and identifying low MW (

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