1 / 47

Advances in PCB Analysis

Advances in PCB Analysis. Presented by Andy Beliveau Region 1 USEPA Office of Environmental Measurement and Evaluation November 2002. 1. Historical Analysis By Aroclor Pattern Recognition. GC/ECD detector with packed and capillary column.

upton
Download Presentation

Advances in PCB Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Advances in PCB Analysis Presented by Andy Beliveau Region 1 USEPA Office of Environmental Measurement and Evaluation November 2002 1

  2. Historical Analysis By Aroclor Pattern Recognition • GC/ECD detector with packed and capillary column. • Relies on matching the standard pattern to the sample pattern. • Quantitated only if the pattern matches (otherwise determined to be a non-detect). • 3 to 5 peaks used for quantitation. 2

  3. Limitations to Aroclor Analysis • Mixtures of Aroclor patterns overlap . • Which peaks represent which Aroclor? • Weathered PCB patterns do not match the standards. • Interpretation is left to the analyst. • Resulting quantitation can be compromised by all of the above. 3

  4. Why use congener analysis • Eliminates interpretation. • Weathering is not a problem. • It is possible to quantitate all peaks . • Sum of the congeners = Total PCB. • Congeners can be quantitated individually. • Dioxin-like congeners can be quantitated. • Non Aroclor PCBs can be identified. • Environmentally modified PCBs can be identifed. 4

  5. Types of Congener Analyses • High resolution GC/ ECD (HRGC) • Long column 60 meters. • Long run time - 60 to 90 minutes . • Requires clean rigorous clean-up of sample. • HRGC/ Low resolution MS (bench top MSD) • Medium column 30 meters. • Medium run time 30-60 minutes. • Eliminates interferences. • Allows for level of chlorination(homologue) analysis. 5

  6. Type of Congener analysis (cont) • HRGC/ High Resolution MS • Requires a expensive instrument and highly qualified operator. • Requires a 10,000 resolution. • Allows for wide concentration range. • Medium length column- 60 meters. • Run time - 30 to 60 minutes. • Requires rigorious clean-up of sample extract for the best results. 6

  7. Congener Analysis by HRGC/ECD • No standard or promulgated EPA method. • Can quantitate 125+ congeners (99%). • Quantitates and identifies by retention time. • Some congeners co-elute depending on column packing, column length, run time, and GC conditions. May require multiple columns to resolve all congeners. • Requires individual standards or known retention time mixed standards. 7

  8. Congeners by HR/GC/LRMS • No Standardized EPA method. Method #680 not promulgated. • Can quantitate 125+ congeners(99%). • Quantitates by retention time and molecular ions for each chlorination level. • Requires individual congener standards. • Can achieve HRGC/ECD detection limits. • Eliminates non PCB interferences. • Can identify non PCB compounds or environmentally modified PCBs. 8

  9. Congeners by HRGC/HRMS • Method 1668a is not yet promugated but is used by a handful of laboratories. • Can quantitate all 209 congeners in water, soil, or biota. • Has a very wide concentration range ( ppm to ppt in soil and ppb to ppqt in water). • Can quantitate WHO dioxin-like congeners. • Can quant total PCB by sum of congeners. • Requires rigorous clean-up and sometimes silica carbon column clean-up. 9

  10. Conclusion: Major uses of congener analysis • Identify congeners in biota and sediments where weathering or metabolizm changes the PCB pattern and Aroclor analysis is unusable. • Confirms the actual concentration of total PCB for cancer risk assessment. • Quantitates WHO doixin-like PCBs for dioxin cancer risks. • Possible to Identify environmentally modified PCBs. 10

  11. Sensing Superfund Chemicals with Recombinant Systems X. Guan,1 W. Luo,2 J. Feliciano,1 R. S. Shetty,1 A. Rothert,1 L. Millner,1 S. K. Deo,1 E. D'Angelo,2 L. G. Bachas,1and S. Daunert1* 1Department of Chemistry and 2Department of Agriculture University of Kentucky Lexington, KY 11

  12. Optical Transducer Whole Cell Whole Cell Sensing System Employing Reporter Protein Signal Analytes Sugars Metal ions Organic pollutants: PCB metabolites Reporter Protein GFP Luciferases Alkaline phosphatase -Galactosidase 12

  13. Whole Cell Sensing System Employing Reporter Protein • REPORTER PLASMID • Reporter Gene • Regulatory protein controlling the expression of the reporter gene • Specificity of the sensing element towards the analyte 13

  14. Analyte Whole Cell-Based Sensing Systems Signal Reporter protein 14

  15. New Bangladesh Disaster:Wells that Pump Poison...New York Times, November 10, 1998 C&EN November 9, 1998 Arsenic Poisoning • Applications of Arsenic • Agriculture • Treatment for diseases • Industrial uses • Long Exposure to Low Doses of Arsenic • Skin Hyperpigmentation and Cancer • Other Cancers • Inhibition of Cellular Enzymes 15

  16. 16

  17. ADP ATP ATP AsO2- ADP ArsA ArsA ArsC Periplasm Membrane Schematic Representation of the Antimonite/Arsenite Pump AsO2- SbO2- Cytoplasm AsO43- ArsB AsO2- SbO2- 17

  18. Sensory Process in Bacteria-Based Sensing Systems reporter gene Regulatory protein bound to promoter inducer (toxin) + reporter gene Regulatory protein bound to inducer Protein Expression Reporter Enzyme Product + Signal Substrate Signal (analyte, M) 18

  19. Reporter Genes Bacterial Luciferase bacterial luciferase Decanal + FMNH2 + O2 Decanoic Acid + FMN + H2O +hn (lmax = 490 nm) b-Galactosidase ELECTROCHEMICAL: b-galactosidase p-aminophenyl-b-galactopyranosidep-aminophenol +galactopyranoside NH+ 2H+ + 2e- HO NH2 O CHEMILUMINESCENCE: b-galactosidase AMPGD hn (lmax = 463 nm) 19

  20. 60000 arsR 50000 ampr pBGD23 40000 lacZ' Light Intensity (counts) 30000 20000 10000 0 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 log (Arsenite, M) Dose-Response Curve for Arsenite (b-galactosidase reporter) E. coli strain AW10 Induction Time: 10 min 20

  21. Sensitivity of the Bioluminescence Sensing System 1 amol of Antimonite = 6 x 105 molecules Bacterial density = 8 x 104 bacteria / 100 µL Therefore, on the average each bacterium detects: 6 x 105 ≈ 7 molecules of Antimonite 8 x 104 21

  22. Electrochemistry Comparison of Sensing Systems -Galactosidase Chemiluminescence h Detection limit for antimonite: 10-7 M Detection limit for antimonite: 10-15 M Bacterial Luciferase Bioluminescence h Detection limit of antimonite : 10-15 M 1011 - fold selective over phosphate, sulfate, arsenate and nitrate 22

  23. 250 Pcup1 gfpuv Fluorescence Intensity ,(a.u.)u. 4000 200 pYEX-GFPuv ampR 3000 150 leu2-d Fluorescence Intensity (%)(5) URA-3 2000 100 log (Copper sulfate, M) 50 1000 0 0 -6.0 -4.0 -3.0 -5.0 -2.0 Zn2+ Co2+ Fe2+ Ni2+ Cu2+ Cd2+ Ag+ Sensing System for Copper Using pYEX-GFPuv 23

  24. In vivo Luminescence Emission of Aequorea victoria 24

  25. 2.5  105 2.0  105 Induction time = 30 min 1.5  105 Fluorescence Intensity (counts) 1.0  105 5.0  104 ampR arsR 0 -3.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.0 -6.5 pSD10 log (Potassium antimonyl tartrate, M) gfpuv Calibration Plot for Antimonite 25

  26. 80000 10-5M 60000 10-6M 40000 Fluorescence Intensity (cps) 20000 Fe(CN)63- SO42- SbO2- As043- Cr2O72- 0 Selectivity Studies 26

  27. Fluorescent Reporter Proteins in Array Detection Protein Excitation Emission  max max GFP 395 (470) 509 EGFP 488 509 BFP 380 440 GFPuv 395 509 YFP 513 527 CFP 433 475 CobA 357 605 RFP 558 583 27

  28. oxidation A P A P HN NH UMT Sirohydrochlorin SAM NH HN P H3C A A A A UMT P P P Urogen III HN H3C N SAM Dihydrosirohydrochlorin (Precorrin-2) NH HN A A P P Trimethylpyrrocorphin P P H3C H3C A A A A P P HN H3C HN H3C N N NH NH N N CH3 A A A A P P P P Production of Fluorescent Porphyrinoid Compounds A -CH2COOH P -CH2CH2COOH SAM - S-adenosyl-L-methionine UMT- uroporphyrinogen methyltransferase III 28

  29. A P A P A C O O H P PBG HN NH ALA A P C O O H deaminase HN NH O dehydratase C O O H HO NH HN A A H N 2 NH HN P A ALA PBG N P P H H N 2 Urogen III HMB A P Urogen III ALA - -aminolevulinic acid PBG - Porphobilinogen synthase HMB- Hydroxymethylbilane Urogen- Uroporphyrinogen -Aminolevulinic Acidin Urogen Pathway 29

  30. 10000 NO ALA NO ALA 1mM ALA 8000 6000 Fluorescence (cps) 4000 2000 0 -2000 Time (h) 0 2 4 6 8 1 0 1 2 Effect of -Aminolevulinic Acid (ALA) 30

  31. 4 5 0 0 0 4 0 0 0 0 O/P 3 5 0 0 0 arsR 3 0 0 0 0 pSD601 Fluorescence (cps) 2 5 0 0 0 cobA ampR 2 0 0 0 0 1 5 0 0 0 1 0 0 0 0 5 0 0 0 Induction time = 1 h Analyte concentration = 5 x 10-6 M 0 - 8 - 7 - 6 - 5 - 4 - 3 log (sodium-m-arsenite) Fluorescence (cps) SO42- NO3- AsO2- SbO2- Br- Cl- PO43- 40 35 30 25 20 15 10 5 0 Calibration Plot (In the Presence of -Aminolevulinic Acid) Selectivity Study 31

  32. OH Cl0-5 Cl0-4 Cl0-5 Cl0-5 OH-PCBs Polychlorinated biphenyls (PCBs) OH Cl0-4 OH OH Cl0-5 Cl0-3 OH PCB-diols Chlorocatechols Chlorinated Organic Compounds • Toxic Effects • Carcinogenic • Neurological • Estrogenic 32

  33. Biphenyl and 3-Chlorocatechol Catabolic Pathways 3-Chlorocatechol Chlorinatedbiphenyl ClcA BphA 2-Chloromuconate Dihydrodiol BphB ClcB 4-Carboxymethylene- 2-en-4-olide 2,3 diOH-ClBP BphC Meta-cleavage compound ClcD Maleylacetic acid BphD Chloro- dihydroxy-benzoate Chlorobenzoate 33

  34. clc R clc A COOH COOH Cl P. putida clc Operon ClcA ClcB ClcD O HOOC COOH OH O COOH O COOH COOH Cl OH Cl - + P/O clc D clc B Clc R 34

  35. Selectivity Studies 1.5 clcR clcA clcB pSMM50R-B Light Intensity (counts 106) 1.0 3-chlorocatechol lacZ ampr 0.5 4-chlorocatechol Catechol Biphenyl 2-chlorophenol 4-chlorophenol 4-chlorobiphenyl 0 -10 -8 -7 -5 -4 -3 -9 -6 log (concentration, M) plasmid courtesy of S.M. McFall & A.M. Chakrabarty 35

  36. Response to Catechol and Chlorocatechols 5 Time of induction: 5 min 4 3-chlorocatechol 3 Light Intensity (counts  105) 2 4-chlorocatechol 1 catechol 0 -10 -9 -8 -7 -6 -5 -4 -3 -2 log (concentration, M) 36

  37. Sample Theoretical Experimental valueSD(mgL-1) value (mgL-1 ) HPLC Bacterial Sensor 0.5 0.50  0.01 0.51  0.05 SFW 1.0 0.96  0.06 10 9.70  0.35 0.5 0.49  0.02 0.48  0.03 1.0 1.03  0.04 SSW 10 9.60  0.52 Analysis of Chlorocatechols in Simulated Fresh Water (SFW) and Simulated Seawater (SSW) and: Not determined 37

  38. HPLC Bacterial Sensor Sample Theoretical Value(mgkg-1) Experimental SD(mgkg-1) Theoretical Value(mgkg-1) Experimental SD(mgkg-1) 0.5 0.49  0.02 0.5 0.51  0.02 Sand 2.0 2.03  0.04 2.0 1.95  0.08 10 9.75  0.55 50 52.0  2.6 Not detectable 0.5 0.52  0.04 0.5 Woolper 2.0 Not detectable 2.0 1.90  0.18 10 9.55  0.75 50 54.0  4.5 Maury Not detectable 0.5 0.43  0.08 0.5 2.0 Not detectable 2.0 2.24  0.30 Organic Potting Soil 0.5 Not detectable 0.5 0.43  0.09 2.0 Not detectable 2.0 1.73  0.28 Analysis of Chlorocatechols in Soils 38

  39. Selectivity Study 8105 4-chlorocatechol 3,4-PCB-diol 4-chloro-PCB-diol biphenyl 4-chlorobiphenyl 3-chloro-benzoic acid 2-chlorophenol or 4-chlorophenol 6105 Light Intensity (counts) 4105 2105 0 39

  40. Field Challenges • Conventional Analytical Techniques • Background Signal • Field-kits • Viability of the cells • Freeze Drying • Liquid Drying Strips (-galactosidase) Vials (luciferase) 40

  41. Response to Arsenite Using -galactosidase Test Strips 0 0.1 0.2 0.5 1 Arsenite (M) 41

  42. Tip of Optical Fiber Bioluminescence Dialysis Membrane Collected by Fiber Bacteria with Induced Bioluminescence Genetically Engineered Cells Analyte Fiber Optic Sensor 42

  43. Microfluidic Platform Prototype CD for Six Simultaneous Analyses 43

  44. 2.5 ampR arsR 2.0 pSD10 1.5 gfpuv Fluorescence (A.U.) 1.0 0.5 0.0 -0.5 -5.5 -5.0 -4.5 -4.0 log (Potassium antimonyl tartrate, M) Calibration Plot for Antimonite in the Microfluidic Set-up 44

  45. Whole Cell Sensing Systems • Feasible to sense Superfund chemicals with recombinant • systems • Employ these whole cell systems in detection of water • and soil samples • Design methods that are amenable to field studies: • Handeling of liquid and freeze dried reagents • Incorporate whole cells in a microfluidics platform • Remote sensing • Incoporation of whole cell sensing systems in • fiber optic set-ups 45

  46. Collaborators Dr. Marc J. Madou Dr. Barry Rosen Dr. Jan Roelof van der Meer 46

  47. Thank You After viewing the links to additional resources, please complete our online feedback form. Thank You Links to Additional Resources 47

More Related