Emily Parry and Thomas Young Department of Civil and Environmental Engineering University of California, Davis

Transcription

1 Emily Parry and Thomas Young Department of Civil and Environmental Engineering University of California, Davis March 2014 University of Arizona, Tucson Environmental Techniques Workshop

2 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

3 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

4 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., H 2 O 2, ozone)

5 Study Design UC Davis Wastewater Treatment Facility Sand Filtration UV Disinfection Flow rate (gpm) Detention Time (s) Added selected surrogate compounds ~ ng/l Influent Samples 4 replicates collected for each tank of wastewater Effluent Samples 4 replicates collected at each condition (flow rate and H 2 O 2 concentration)

6 Target Compounds Selected Compound Name Class Reason Carbamazepine (CBZ) anticonvulsant Resistant to photo-degradation Diclofenac (DFC) analgesic, anti-inflammatory Common analgesic Ibuprofen (IBP) analgesic, anti-inflammatory Good 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) disinfectant Some 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,

7 Extraction and Analysis Sample Processing 4 replicates per flow/h 2 O 2 condition 1 L samples filtered with GF/B 1 µm filter Extraction Method Add 1 g EDTA Adjust ph to 4 Add 13 C labeled trimethoprim & diclofenac as surrogates HLB SPE Extraction Concentrate to 1 ml LC Method 0.1% Acetic Acid as additive in mobile phases works in both negative and positive modes

8 Target compound removal at 12 gpm Fraction Remaining Eff 0 mg/l Eff 95 mg/l Eff 191 mg/l Compound

9 Target compound removal at 6 & 35 gpm Eff 210 mg/l 35 Eff 204N mg/l 6 Eff 153 mg/l Fraction Remaining Compound

10 Steady state OH concentration estimates [OH] Radical Concentration (pmol/l) Treatment CBZ 3 CBZ 1 CBZ 3 MTP 1 MTP 2 DEET 1 TMP 1 TMP 2 GFL 1 AVG 12 Eff Eff Eff Eff Eff 204 N Eff Ben 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), Wols, 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), De 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),

11 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) Suspect Screening Statistical Analysis

12 Overview of Molecular Features Molecular weight (amu) 3,681 molecular features Retention time (min)

13 Effect of H 2 O 2 addition at 12 gpm Removal 12 eff95 Removal 12eff0

14 Removal comparison 6 and 35 gpm Removal 35eff204N Removal 6eff153 Removal 35eff210

15 Suspect Screening Screen with an exact mass library of contaminants 3681 Features from MPP 537 database matches Match score greater > with a score > 69.5 RT prediction filter 74 unique features passed RT filter MS/MS experiments 8 standards purchased

16 Constructing Compound Database Byproducts predicted for our target compounds by K. Li and F. Zeng (University of Georgia) Database contains 1368 compounds

17 RT Prediction Filter Retention time (min) fit 95% Prediction Interval Target compounds log Kow (episuite prediction)

18 Molecular Structure Correlator

19 Performance of Target Compounds Database matched after MPP filtering Carbamazepine Diclofenac Fluoxetine Gemfibrozil Sulfamethoxazole Triclosan Trimethoprim Tetracycline Metoprolol Erythromycin Phenytoin Deet Ibuprofen tetracycline What concentration would need to be present in order pass filters? Score > 69.5 Carbamazepine Diclofenac Fluoxetine Gemfibrozil Sulfamethoxazole Triclosan Trimethoprim MS/MS match Carbamazepine Diclofenac Fluoxetine Gemfibrozil Sulfamethoxazole Triclosan

20 Filtering method limit of detection 1000 ng/l Carbamazepine Diclofenac Fluoxetine Gemfibrozil Sulfamethoxazole Triclosan Trimethoprim Tetracycline Metoprolol Erythromycin Phenytoin Deet Ibuprofen 500 ng/l Carbamazepine Diclofenac Fluoxetine Gemfibrozil Sulfamethoxazole Triclosan Trimethoprim Tetracycline Metoprolol Erythromycin Phenytoin Deet Ibuprofen 200 ng/l Carbamazepine Diclofenac Fluoxetine Gemfibrozil Sulfamethoxazole Triclosan Trimethoprim Tetracycline Metoprolol Erythromycin Phenytoin Deet Ibuprofen 100 ng/l Carbamazepine Diclofenac Fluoxetine Gemfibrozil Sulfamethoxazole Triclosan Trimethoprim Tetracycline Metoprolol Erythromycin Phenytoin Deet Ibuprofen

21 Identifications 8 Standards purchased > 5 compounds confirmed w/ msms spectral matches Efavirenz lamictal flunixin hydrochlorothiazide Mefenamic acid Appear on Howard & Muir s list of PCPPs not yet found in the environment but predicted to be persistent/bioaccumulative Howard, P.; Muir, D., Identifying New Persistent and Bioaccumulative Organics Among Chemicals in Commerce II: Pharmaceuticals. Environmental Science & Technology 2011, 45, (16),

22 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,

23 Mass Defect Filtering Exact Mass NominalMass MassDefect Sulfamethoxazole C 10 H 11 N 3 O 3 S Mass Shift (Da): +48 Da, 45 Da Mass Defect Shift (Da): , Range of values from Table 1 of Zhang et al Sulfamethoxazole MDF 196 Nominal Mass Mass Defect Byproduct Identified 4 nitrosulfamethoxazole C 10 H 9 N 3 O 5 S Effluent ONLY Features Post MDF Filter Possible Oxidative Rxn RT and MS/MS match to analytical standard

24 Diclofenac predicted byproducts

25 Diclofenac Degradation Products O 7.0E+05 Cl H N OH 6.0E+05 Ion counts 5.0E E E+05 12inf avg 12Eff0 avg 12Eff95 avg 12eff191 avg 35inf avg Cl Diclofenac 2.0E+05 35eff204N avg 35eff210 avg O 1.0E+05 6Eff153 avg OH 0.0E+00 2o Diclofenac Degradation Product ID 2o NH 2

26 Conclusions 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

27 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

28 Acknowledgments Funding from the National Science Foundation Graduate Research Program (NSF GRFP, Grant # )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