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