emily parry and thomas young - agilent parry... · presentation outline • aop method, sampling...

Emily Parry and Thomas YoungDepartment of Civil and Environmental 

EngineeringUniversity of California, Davis

University of Arizona, TucsonEnvironmental Techniques Workshop

March 2014

Research Objectives

• Develop and refine methodology for using HR‐MS to optimize an AOP reactor performance and identify unknowns and treatment byproducts in wastewater– Optimize reactor with targeted approach– Optimize extraction & chromatography for target compounds

– Formalize HR‐MS data analysis filtering workflow• Determine “limit of detection” of methodology• Byproduct identification techniques

Presentation Outline

• AOP method, sampling strategy, extraction and instrumental analysis

• Targeted evaluation of the reactor• Hybrid target/nontarget approach

– Generation of complete list of molecular features– Visualize reactor performance – Apply molecular database, spectral matching and predicted 

retention time to narrow candidates for identification– Collect MS/MS data to confirm tentative identification of 

compounds & compare with standards (if available). Evaluate reactor performance on newly identified compounds

– Evaluate approach with target compounds• Discuss byproduct identification strategies

Innovative UV reactor

• UV lamps outside quartz reactor vessel

• High velocity water input produces high UV doses with minimal retention times and lamp intensities

• Air core promotes turbulence• Vigorous mixing ideal for introduction of oxidants (e.g., H2O2, ozone)

Study DesignUC Davis Wastewater Treatment Facility

Sand Filtration

Flow rate (gpm)

Detention Time (s)

6 60.912 30.535 10.4

UV Disinfection

Added selected surrogate compounds ~200‐400 ng/L

Effluent Samples4 replicates collected at each condition (flow rate and H2O2concentration)

Influent Samples4 replicates collected for each tank of wastewater

Target Compounds SelectedCompound Name Class Reason

Carbamazepine (CBZ) anticonvulsant Resistant to photo-degradation

Diclofenac (DFC) analgesic, anti-inflammatory Common analgesic

Ibuprofen (IBP) analgesic, anti-inflammatoryGood indictor of OH radical exposure;

Ionizes in negative mode.

DEET* topical insect repellant Indicator compound – good removal

Phenytoin* (PHN) anticonvulsant Indicator compound – good removal

Trimethoprim (TMP) antibiotic Commonly found in wastewater

Tetracycline (TCY) antibiotic Represents a class of antibiotics

Triclosan (TCS) disinfectantSome literature on byproducts; commonly

found

Sulfamethoxazole (SMZ) antibiotic Represents a class of antibiotics

Metoprolol (MTP) Beta-blocker Represents common class of compounds

Gemfibrozil (GFL) lipid regulator Ionizes in negative mode

Erythromycin (ERY) antibiotic Represents a class of antibiotics

*Good removal indicators > 90% - other alkyl aromatic with moieties admenable to oxidative attack will be removed. Intermediate removal – represent saturated aliphatic OH radicals concentration needs to be increased to achieve > 90%Environ. Sci. Technol. 2009, 43, 6242–6247

Extraction and Analysis 

• 4 replicates per flow/H2O2 condition

• 1 L samples filtered with GF/B 1 µm filter

Sample Processing

LC Method0.1% Acetic Acid as additive in mobile phases – works in both negative and positive modes

Extraction Method•Add 1 g EDTA•Adjust pH to 4•Add 13C labeled trimethoprim & diclofenac as surrogates•HLB SPE Extraction•Concentrate to 1 mL

Target compound removal at 12 gpm

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Fractio

n Re

maining

Compound

Eff ‐ 0 mg/L

Eff ‐ 95 mg/L

Eff ‐ 191 mg/L

Target compound removal at 6 & 35 gpm

‐0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Fractio

n Re

maining

Compound

35Eff‐210 mg/L

35 Eff‐204N‐ mg/L

6 Eff‐ 153 mg/L

Steady state OH concentration estimates

[OH] Radical Concentration (pmol/L)Treatment CBZ3 CBZ1 CBZ3 MTP1 MTP2 DEET1 TMP1 TMP2 GFL1 AVG12 Eff 0 0.0 ‐0.1 ‐0.1 0.0 0.0 ‐0.3 0.0 0.0 0.1 0.012 Eff 95 1.0 2.0 2.1 2.4 2.6 2.8 1.6 2.3 2.3 2.112 Eff 191 0.9 1.9 2.0 2.4 2.5 2.7 1.0 1.4 2.6 1.935 Eff 210 3.2 6.6 7.2 6.9 7.3 8.9 10.9 15.4 5.1 8.035 Eff 204 N 1.9 3.8 4.2 4.4 4.7 6.0 1.8 2.6 5.6 3.96 Eff 153 1.5 3.0 3.3 3.2 3.4 4.7 2.3 3.3 2.6 3.0

1Ben Abdelmelek, S.; Greaves, J.; Ishida, K.; Cooper, W.; Song, W., Removal of Pharmaceutical and Personal Care Products from Reverse Osmosis Retentate Using Advanced Oxidation Processes. Environmental Science & Technology 2011, 45, (8), 3665‐3671.2Wols, B.; Hofman‐Caris, C., Review of photochemical reaction constants of organic micropollutants required for UV advanced oxidation processes in water. Water Research 2012, 46, (9), 2815‐2827.3De Laurentiis, E.; Chiron, S.; Kouras‐Hadef, S.; Richard, C.; Minella, M.; Maurino, V.; Minero, C.; Vione, D., Photochemical Fate of Carbamazepine in Surface Freshwaters: Laboratory Measures and Modeling. Environmental Science & Technology 2012, 46, (15), 8164‐8173.

Feature Identification and Filtering

Recursive Analysis

Identify molecular features based on analyst selected parameters

(Mass Hunter)

Filter and align features across all replicates; Subtract background 

ions and create recursive filter(Mass Profiler Professional )

Identify features using recursive filter(Mass Hunter)

Statistical AnalysisSuspect Screening

Overview of Molecular Features

Retention time (min)

Mol

ecul

ar w

eigh

t (am

u)

3,681 molecular features

Effect of H2O2 addition at 12 gpm

Removal 12eff0

Removal 12 eff95

Removal comparison 6 and 35 gpm

Removal 35eff204N

Removal 6eff153

Removal 35eff210

Suspect Screening

Match score greater >69.5

RT prediction filter

MS/MS experiments

Screen with an exact mass library of contaminants

3681 Features from MPP537 database matches

147 with a score > 69.5

74 unique features passed RT filter

8 standards purchased

Constructing Compound Database

Byproducts predicted for our target compounds by K. Li and F. Zeng(University of Georgia)

Database contains 1368 compounds 

RT Prediction Filter

‐2

0

2

4

6

8

10

12

14

‐4 ‐2 0 2 4 6 8

Retention tim

e (m

in)

log Kow (episuite prediction)

fit

95% Prediction Interval

Target compounds

Molecular Structure Correlator

Performance of Target Compounds

Database matched after MPP filteringCarbamazepineDiclofenacFluoxetineGemfibrozilSulfamethoxazoleTriclosanTrimethoprimTetracyclineMetoprololErythromycinPhenytoinDeetIbuprofentetracycline

Score > 69.5CarbamazepineDiclofenacFluoxetineGemfibrozilSulfamethoxazoleTriclosanTrimethoprim

MS/MS matchCarbamazepineDiclofenacFluoxetineGemfibrozilSulfamethoxazoleTriclosan

What concentration would need to be present in order pass filters?

Filtering method limit of detection

1000 ng/LCarbamazepineDiclofenacFluoxetineGemfibrozilSulfamethoxazoleTriclosanTrimethoprimTetracyclineMetoprololErythromycinPhenytoinDeetIbuprofen

500 ng/LCarbamazepineDiclofenacFluoxetineGemfibrozilSulfamethoxazoleTriclosanTrimethoprimTetracyclineMetoprololErythromycinPhenytoinDeetIbuprofen

200 ng/LCarbamazepineDiclofenacFluoxetineGemfibrozilSulfamethoxazoleTriclosanTrimethoprimTetracyclineMetoprololErythromycinPhenytoinDeetIbuprofen

100 ng/LCarbamazepineDiclofenacFluoxetineGemfibrozilSulfamethoxazoleTriclosanTrimethoprimTetracyclineMetoprololErythromycinPhenytoinDeetIbuprofen

Identifications8 Standards purchased ‐> 5 compounds confirmed w/ msms spectral matches

Efavirenz lamictal flunixin

hydrochlorothiazide Mefenamic acidHoward, P.; Muir, D., Identifying New Persistent and Bioaccumulative Organics Among Chemicals in Commerce II: Pharmaceuticals. Environmental 

Science & Technology 2011, 45, (16), 6938‐6946.

Appear on Howard & Muir’s  list of PCPPs not yet found in the environment but predicted to be persistent/bioaccumulative

Byproducts ‐Mass Defect Filtering

• Mass Defect = Exact Mass – Integer Mass

• Used to identify drug metabolites in biological matrices

• Will apply it to identify byproducts from AOPs and other processes

Zhang,et al., Mass defect filter technique and its applications to drug metabolite identification by high‐resolution mass spectrometry, J. Mass. Spectrom. 2009, 44, 999–1016

Mass Defect Filtering

Exact Mass  Nominal Mass Mass Defect

236.09496 236 0.09496

SulfamethoxazoleC10H11N3O3S

Mass Shift (Da): +48 Da, ‐45 DaMass Defect Shift (Da): ‐0.0367, +0.0313

Range of values from Table 1 of Zhang et al. 2009

Sulfamethoxazole MDF 196  Nominal Mass  284

0.05346 Mass Defect  0.12796

Effluent  ONLY Features

Post MDF Filter

Possible Oxidative Rxn

275 13 1

Byproduct Identified4‐nitrosulfamethoxazole

C10H9N3O5S

RT and MS/MS match to analytical standard

Diclofenac predicted byproducts

Diclofenac Degradation Products

0.0E+00

1.0E+05

2.0E+05

3.0E+05

4.0E+05

5.0E+05

6.0E+05

7.0E+05

2o

Ion coun

ts

Diclofenac Degradation Product ID

12inf‐avg

12Eff0‐avg

12Eff95‐avg

12eff191‐avg

35inf‐avg

35eff204N‐avg

35eff210‐avg

6Eff153‐avg

NH2

OH

O

2o

OH

O

HN

Cl

Cl

Diclofenac

• HR‐MS data can provide a full view of AOP reactor performance

• A combination of database filters and statistical approaches can identify compounds of interest within the large high resolution MS dataset  

• A holistic view of microconstituent treatment requires consideration of both compound destruction and byproduct formation– Mass defect filtering and AOP reaction model are promising 

techniques for finding byproducts

Conclusions

Future Work

• Refine byproduct identification strategy• Connect byproducts to toxicity predictions• Apply similar approaches to identifying and quantifying byproducts in natural and other engineered systems

Acknowledgments

• Funding from the National Science Foundation Graduate Research Program (NSF‐GRFP, Grant # 1148897)and the National Institute of Environmental Health Sciences (NIEHS)

• Prof. Bassam Younis (UC Davis)• Prof. Ke Li and Fang Zeng (U Georgia)• Staff at the UC Davis Wastewater Treatment Plant 

(Michael Fang, Brad Butterworth)• Daniel Cuthbertson for his assistance with MPP• Jenny Mital & Laura Mahoney for assistance sampling