advances in pcb analysis

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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. - PowerPoint PPT Presentation

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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. 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

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

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

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

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

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

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

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

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

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,1 and

S. Daunert1*

1Department of Chemistry and2Department of Agriculture

University of KentuckyLexington, KY

11

OpticalTransducer

WholeCell

Signal

AnalytesSugars

Metal ions

Organic pollutants:

PCB metabolites

Reporter ProteinGFP

Luciferases

Alkaline phosphatase

-Galactosidase

Whole Cell Sensing System Employing Reporter Protein

12

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

•Whole Cell-Based Sensing SystemsWhole Cell-Based Sensing Systems

Reporter Reporter proteinprotein

SignalSignalAnalyteAnalyte

14

Arsenic Poisoning

C&EN November 9, 1998

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

New Bangladesh Disaster:Wells that Pump Poison...New York Times, November 10, 1998

15

Arsenic contamination in the USA

U. S. Geological Survey, Fact Sheet FS 063-00, May 2000

16

ArsAArsA

PeriplasmMembrane

ArsB

AsO43-

AsO2-

AsO2- SbO2

-

AsO2- SbO2

-

ATPATP

Cytoplasm

ADP

ADP

Schematic Representation of the Antimonite/Arsenite Pump

ArsC

17

reporter geneRegulatory proteinbound to promoter inducer (toxin)

reporter gene +

Protein Expression Regulatory proteinbound to inducer

Reporter Enzyme

Substrate Product + Signal

Sig

nal

(analyte, M)

Sensory Process in Bacteria-Based Sensing Systems

18

Bacterial Luciferase

Decanal + FMNH2 + O2 Decanoic Acid + FMN + H2O + hmax = 490 nm)bacterial luciferase

p-aminophenyl-galactopyranosidep-aminophenol + galactopyranoside

GalactosidaseELECTROCHEMICAL:

HO NH2 O NH + 2H+ + 2e-

galactosidase

CHEMILUMINESCENCE:AMPGD hmax = 463 nm)

galactosidase

Reporter Genes

19

log (Arsenite, M)

Lig

ht

Inte

nsi

ty (

cou

nts

)

-3.5-4.5-5.5-6.5-7.5-8.5-9.50

10000

20000

30000

40000

50000

60000

Dose-Response Curve for Arsenite (-galactosidase reporter)

E. coli strain AW10Induction Time: 10 minarsR

lacZ'

ampr

pBGD23

20

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

8 x 104≈ 7 molecules of

Antimonite

Sensitivity of the Bioluminescence Sensing System

21

Comparison of Sensing Systems

-Galactosidase

Detection limit for antimonite: 10-15 M

Bacterial Luciferase

Detection limit of antimonite : 10-15 M

BioluminescenceBioluminescence

hh

Chemiluminescence

h

Detection limit for antimonite: 10-7 M

Electrochemistry

1011 - fold selective over phosphate, sulfate, arsenate and nitrate

22

Sensing System for Copper Using pYEX-GFPuv

0

50

100

150

200

250

ZnZn2+2+ FeFe2+2+CoCo2+2+ CuCu2+2+ CdCd2+2+ AgAg++NiNi2+2+F

luo

resc

ence

Inte

nsi

ty (

%)(

5)

log (Copper sulfate, M)

-6.0 -5.0 -4.0 -3.0 -2.0

0

1000

2000

3000

4000F

luo

res

ce

nc

e I

nte

ns

ity

,(a

.u.)

u.

Pcup1

gfpuv

leu2-dURA-3

ampR

pYEX-GFPuv

23

In vivo Luminescence Emission of Aequorea victoria

24

Calibration Plot for Antimonite

ampR arsR

gfpuv

pSD10

0

5.0 104

1.0 105

1.5 105

2.0 105

2.5 105

-6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0log (Potassium antimonyl tartrate, M)

Induction time = 30 minInduction time = 30 min

Flu

ore

sce

nc

e In

ten

sit

y(c

ou

nts

)

25

0

20000

40000

60000

80000

10-6M

SbO2- As04

3- Cr2O72- Fe(CN)6

3- SO42-

Flu

ore

scen

ce I

nte

nsi

ty (

cps)

Flu

ore

scen

ce I

nte

nsi

ty (

cps)

10-5M

Selectivity StudiesSelectivity Studies

26

Fluorescent Reporter Proteins in Array Detection

Protein Excitation Emission max max

GFP 395 (470) 509EGFP 488 509BFP 380 440GFPuv 395 509YFP 513 527CFP 433 475CobA 357 605RFP 558 583

27

A -CH2COOH

P -CH2CH2COOH

SAM - S-adenosyl-L-methionine

UMT- uroporphyrinogen methyltransferase III

UMT

SAMHN

NH N

N

PA

A

P

A

P

A

P

H3C

H3C

CH3

Trimethylpyrrocorphin

HN

NH N

N

PA

P

A

P

A

P

A

H3C

H3C

Sirohydrochlorin

oxidation

HN

NH

N

PA

P

A

P

A

P

A

H3C

H3C

HN

Dihydrosirohydrochlorin(Precorrin-2)

SAM

UMT

Urogen III

HN

NH

NH

P A

PA

P

A

P

AHN

Production of Fluorescent Porphyrinoid Compounds

28

-Aminolevulinic Acid in Urogen Pathway

ALA

NH2

O

COOH

dehydratase

ALA

ALA - -aminolevulinic acid

HN

NH

NH

P A

PA

P

A

P

AHN

Urogen III

Urogen- Uroporphyrinogen

deaminase

PBG

PBG

NH

COOH

COOH

NH2

PBG - Porphobilinogen

synthase

Urogen III

HMB

HN

NH

NH

P A

PA

A

P

P

AHN

HO

HMB- Hydroxymethylbilane

29

Effect of -Aminolevulinic Acid (ALA)

Flu

ore

sce

nc

e (c

ps)

0 2 4 6 8 10 12-2000

10000

8000

6000

4000

2000

0

Time (h)

NO ALA1mM ALA

NO ALA

30

-8 -7 -6 -5 -4 -30

5000

10000

15000

20000

25000

30000

35000

40000

45000

Flu

ore

scen

ce (

cps)

log (sodium-m-arsenite)

Calibration Plot (In the Presence of -Aminolevulinic Acid)

Selectivity Study

Flu

ore

scen

ce (

cps)

0

5

10

15

20

25

30

35

40

Br- Cl- PO43- SO4

2- AsO2- SbO2

-NO3-

Induction time = 1 hAnalyte concentration = 5 x 10-6 M

ampR

pSD601cobA

O/P

arsR

31

Chlorinated Organic Compounds

OH-PCBs

OHOH

Cl0-4

Chlorocatechols

Cl0-5Cl0-5

OHCl0-4Cl0-5

OH

PCB-diols

OHCl0-3Cl0-5

Polychlorinated biphenyls (PCBs)

Toxic Effects

• Carcinogenic

• Neurological

• Estrogenic

32

Biphenyl and 3-Chlorocatechol Catabolic Pathways

Chlorinated biphenyl

Dihydrodiol

2,3 diOH-ClBP

Meta-cleavagecompound

Chlorobenzoate Chloro-dihydroxy-benzoate

3-Chlorocatechol

2-Chloromuconate

4-Carboxymethylene-2-en-4-olide

Maleylacetic acid

BphA

BphB

BphC

BphD

ClcA

ClcB

ClcD

33

P. putida clc Operon

clc B clc DP/O

Clc R

OH

Cl

COOH

O

OHCOOH

ClcA ClcB ClcD

O

HOOC

O

- +

COOH

COOH

Cl

COOH

COOH

Cl

clc R clc A

34

-10 -9 -8 -7 -6 -5 -4 -3

1.5

1.0

0.5

0

L

igh

t In

ten

sit

y (c

ou

nts

1

06)

log (concentration, M)

3-chlorocatechol

4-chlorocatechol

4-chlorobiphenyl

Selectivity Studies

plasmid courtesy of S.M. McFall & A.M. Chakrabarty

clcR

clcA

clcB

lacZampr

pSMM50R-B

CatecholBiphenyl2-chlorophenol4-chlorophenol

35

-10 -9 -8 -7 -6 -5 -4 -3 -2

5

4

3

2

1

0

Lig

ht

Inte

ns

ity

(co

un

ts

10

5)

log (concentration, M)

Response to Catechol and Chlorocatechols

Time of induction: 5 min

3-chlorocatechol

catechol

4-chlorocatechol

36

Analysis of Chlorocatechols in Simulated Fresh Water (SFW) and Simulated Seawater (SSW)

0.5

9.60 0.5210

SSW 1.03 0.041.0

0.48 0.030.49 0.020.5

9.70 0.3510

SFW 0.96 0.061.0

0.51 0.050.50 0.01

Bacterial SensorHPLCvalue (mgL-1 )

Experimental valueSD(mgL-1)TheoreticalSample

37

Analysis of Chlorocatechols in Soils

 

1.73 0.28 2.0Not detectable2.0

0.43 0.090.5Not detectable0.5Organic Potting Soil

 2.24 0.30 2.0Not detectable2.0  0.43 0.080.5Not detectable0.5Maury  54.0 4.550

9.55 0.7510

1.90 0.182.0Not detectable2.0

0.52 0.040.5Not detectable0.5Woolper

52.0 2.650

9.75 0.5510

 1.95 0.082.02.03 0.042.0

 

0.51 0.020.50.49 0.020.5Sand

Experimental SD(mgkg-1)

Theoretical Value(mgkg-1)

Experimental SD(mgkg-1)

TheoreticalValue(mgkg-1)

Bacterial SensorHPLC

Sample

38

Lig

ht

Inte

ns

ity

(co

un

ts)

0

2105

4105

6105

8105

4-chlorocatechol 3,4-PCB-diol 4-chloro-PCB-diol biphenyl 4-chlorobiphenyl 3-chloro-benzoic acid 2-chlorophenol or 4-chlorophenol

Selectivity Study

39

Field Challenges

Background Signal

Freeze Drying

Liquid Drying

Vials (luciferase)

Strips (-galactosidase)

Viability of the cells

Field-kits

Conventional Analytical Techniques

40

Response to Arsenite Using -galactosidase

Test Strips

0 0.1 0.2 0.5 1Arsenite (M)

41

Bacteria with Induced

BioluminescenceCollected by Fiber

Dialysis Membrane

Analyte

BioluminescenceGenetically Engineered

Cells

Tip of OpticalFiber

Fiber Optic Sensor

42

Microfluidic Platform

Prototype CD for Six Simultaneous Analyses

43

Calibration Plot for Antimonitein the Microfluidic Set-up

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-5.5 -5.0 -4.5 -4.0

log (Potassium antimonyl tartrate, M)

Flu

ore

scen

ce

(A.U

.)ampampRR

arsRarsR

gfpuv pSD10pSD10

44

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

Collaborators

Dr. Marc J. MadouDr. Barry RosenDr. Jan Roelof van der Meer

46

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