The developed LC-MS/MS method was used to screen collected water

samples. Results of quantified PPCP are shown in Figures 3 and 4. All

findings were identified by comparing the MRM ratio of the unknown sample

with the average MRM ratio of standard injections.

Figure 6 shows the quantitative results of Benzoylecgonine, a metabolite of

Cocaine, in the studied water samples. All drinking water samples had a

concentration of benzoylecgonine below 5 ng/L. As expected,

Benzoylecgonine was found in rivers running through major cities at

concentrations of up to 200 ng/L indicating the abuse of cocaine. The

concentration of Benzoylecgonine in water samples collected in less urban

and wilderness areas was much lower with the exception of one creek and

one lake in popular vacation destinations. The concentration of

benzoylecgonine reflects the amount collectively excreted in urine and can be

used the estimate drug consumption. Also when analyzed over time drug

consumption habits can be investigated.7

Figures 7 and 8 show the findings of caffeine and Sildenafil in studied waters.

The water sample with a question mark was collected from a holy water basin

in a church in Roma, Italy. The summary of detected compounds in the holy

water sample is presented in Table 1.

Non-Targeted Screening using the TripleTOF® 5600 system

Full scan MS and MS/MS allow retrospective screening and identification of

non-target and unexpected compounds. Acquired chromatograms are very

rich in information and can easily contain thousands of ions from both any

compounds present in the sample as well as from the sample matrix itself.

Thus, powerful software tools are needed to explore such data to identify the

unexpected compound. Two data processing workflows are possible. 1)

Comparison of two similar samples (sample and control) and 2) Statistical

comparison of a huge set of samples (metabolomic profiling)

1) The MasterView™ software was used to compare two different water

samples. A non-target peak finding algorithm is used to automatically

generate extracted ion chromatograms (XIC). The user can then define an

intensity threshold to compare both samples. Figure 10 shows an example of

comparing two lake water samples. A total of 36 signals were found in the

urban lake water which were 10x higher than in the control water. DEET was

automatically identified using MS/MS library searching (Figure 9),

OVERVIEW

A wide range of Pharmaceuticals and Personal Care Products (PPCP) were

determined in river water samples using Liquid Chromatography tandem

Mass Spectrometry (LC-MS/MS). Different water samples were injected

directly into the LC-MS/MS to quantify PPCP at low parts-per-trillion levels

(ng/L). Multiple Reaction Monitoring was used for detection on an AB SCIEX

QTRAP® 5500 system for highest sensitivity and selectivity with the

Scheduled MRM™ algorithm activated for best accuracy and reproducibility.

In addition high resolution and accurate mass MS and MS/MS data acquired

on an AB SCIEX TripleTOF® 5600 system was screened for non-target and

unexpected pollutants.

INTRODUCTION

PPCP encompass a wide range of pollutants, including Endocrine Disrupting

Compounds (EDC), pesticides, hormones, antibiotics, drugs of abuse, x-ray

contrast agents, drinking water disinfection by-products to name a few. In

order to properly assess the effects of these compounds on our environment,

it is necessary to accurately monitor their presence. The diversity of chemical

properties of these compounds makes method development challenging. LC-

MS/MS is able to analyze polar, semi-volatile, and thermally labile compounds

covering a wide molecular weight range. In addition, state-of the-art LC-

MS/MS instruments operated in selective Multiple Reaction Monitoring (MRM)

mode, offer unmatched selectivity and sensitivity to quantify PPCP

reproducibly at trace levels without time consuming and extensive sample

preparation.1-4

A method is presented for analyzing 80 EDC and PPCP compounds using

LC-MS/MS. This method is a straight forward approach for the analysis and

identification of these compounds with excellent sensitivity and ruggedness.5

More recently there is a growing interest from environmental researchers to

also screen for and identify non-targeted compounds in environmental

samples, including metabolites and degradates, but also completely

unexpected pollutants. The AB SCIEX TripleTOF® 5600 ystem is capable of

performing highly sensitive and fast MS scanning experiments to search for

unknown molecular ions while also performing selective and characteristic

MS/MS scanning for further compound identification and, therefore, is the

instrument of choice for this challenging task. General unknown screening

workflows do not use a target analyte list and compound detection is not

based on any a priori knowledge, including retention times and information on

possible molecular and fragment ions. Therefore, acquired chromatograms

are very rich in information and can easily contain thousands of ions from

both any compounds present in the sample as well as from the sample matrix

itself. Thus, powerful software tools are needed to explore such data to

identify the unexpected compound.

Data processing workflows are presented to successfully identify unexpected

environmental pollutants using non-target peak finding, statistical data

analysis, empirical formula finding, and MS/MS fragment interpretation.6

EXPERIMENTAL

Sampling and Sample Preparation

More than 70 water samples in different cities and countries from different

type of waters, including drinking water, creeks, rivers, lakes, sea etc were

collected by different scientist. Samples were kept frozen until analysis.

LC Separation

A Shimadzu UFLCXR system was used with a Phenomenex LUNA 2.5u

C18(2)-HST 100 x 3 mm column and fast gradients of water and acetonitrile

with 0.1% formic acid at a flow rate of 0.8 mL/min. An sample volume of 100

µL was injected directly.

MS/MS Detection

The AB SCIEX QTRAP® 5500 LC/MS/MS system with Turbo V™ source and

Electrospray Ionization (ESI) probe was used. The mass spectrometer was

operated in MRM mode using the Scheduled MRM™ algorithm. The AB

SCIEX TripleTOF® 5600 LC/MS/MS system with DuoSpray™ source

operated in ESI mode. An IDA experiment was used with a TOF-MS survey

(100 ms) and up to 10 dependent MS/MS scans (accumulation time of 50 ms)

using Collision Energy Spread (35 V ± 15 V).

RESULTS AND DISCUSSION

Targeted Quantitation using the QTRAP® 5500 system

The combination of a small particle size column (2.5 μm), high flow rate (0.8

mL/min), and large injection volume (100 μL), with high sensitivity MS/MS

detection allowed the direct injection of water samples and detection of PPCP

with Limits of Detection (LOD) in the low parts per trillion (ppt) range. Two

MRM transitions were monitored for each of the 80 analytes to quantify and

identify using MRM ratio calculation. The Scheduled MRM™ algorithm

automatically adjusts dwell times for best Signal-to-Noise (S/N) based on

retention time and targeted cycle time input. Two MRM transitions were

monitored for each analyte, the most sensitive, first MRM transition was used

for quantitation while the second MRM transition was used for qualitative

identification based on ion ratio calculation.

Figure 1. The Scheduled MRM™ Algorithm uses the knowledge of the elution of each analyte to monitor

MRM transitions only during a short retention time window. This allows many more MRM transitions to be

monitored in a single LC run, while maintaining maximized dwell times and optimized cycle time.

empirical formula finding, and ChemSpider searching, the structure found in

ChemSpider was automatically fragmented and compared against the MS/MS

spectrum (Figure 10).

2) In the case that a proper control sample is not available or that

contaminations have to be identified at lower concentrations a statistical

approach is needed. A large set of water samples was compared using

principal components analysis (PCA) in MarkerView™ software and DEET was

identified using empirical formula calculation follow by an online database

search. (Figure 11)6

REFERENCES

1 Brett J. Vanderford et al.: ‘Analysis of Endocrine Disruptors, Pharmaceuticals,

and Personal Care Products in Water Using Liquid Chromatography/Tandem

Mass Spectrometry’ Anal. Chem. 75 (2003) 6265-6274 2 J. Paul E. Stackelberg et al.: ‘Persistence of pharmaceutical compounds and

other organic wastewater contaminants in a conventional drinking-water-

treatment plant’ Science of the Total Environment 329 (2004) 99-113 3 Susan D. Richardson and Thomas Ternes: ‘Water Analysis: Emerging

Contaminants and Current Issues’ Anal. Chem. 77 (2005) 3807-3838 4 M. Gros et al.: ‘Tracing Pharmaceutical Residues of Different Therapeutic

Classes in Environmental Waters by Using Liquid Chromatography /

Quadrupole-Linear Ion Trap Mass Spectrometry and Automated Library

Searching’ Anal. Chem. 81/3 (2009) 898-912 5 A. Schreiber et al.: ‘Quantitation and Identification of Pharmaceuticals and

Personal Care Products (PPCP) in Water Samples’ Application Note AB SCIEX

(2010) #1790210-01 6 A. Schreiber et al.: ‘Metabolomic Profiling of Accurate Mass LC-MS/MS Data

to Identify Unexpected Environmental Pollutants’ Application Note AB SCIEX

(2010) #0460210-02 7 S. Castiglioni et al.: ‘Mass spectrometric analysis of illicit drugs in wastewater

and surface water’ Mass Spectrometry Reviews 27 (2008) 378-394

TRADEMARKS/LICENSING

For Research Use Only. Not for use in diagnostic procedures.

The trademarks mentioned herein are the property of AB Sciex Pte. Ltd. or

their respective owners.

AB SCIEX™ is being used under license.

© 2014 AB SCIEX.

Screening and Quantitation of Targeted and Non-

targeted Environmental Pollutants in Water

Samples Tom Knapman, Ashley Sage & Dan McMillan

1AB SCIEX, Warrington UK

XIC of +MRM (150 pairs): 167.2/85.1 amu Expected RT: 0.6 ID: Cyromazine 1 from Sample 2 (Std 10) of Data real samples sMRM_fast.wiff... Max. 3.6e5 cps.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0Time, min

0.0

5.0e5

1.0e6

1.5e6

1.7e6

In

te

ns

it

y,

c

ps

0.58

XIC of +MRM (150 pairs): 167.2/85.1 amu Expected RT: 0.6 ID: Cyromazine 1 from Sample 2 (Std 10) of Data real samples sMRM_fast.wiff... Max. 3.6e5 cps.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0Time, min

0.0

5.0e5

1.0e6

1.5e6

1.7e6

In

te

ns

it

y,

c

ps

0.58

+MRM (150 pairs): 9.032 to 9.408 min from Sample 2 (Std 10) of Data real samples sMRM_fast.wiff (Turbo Spray) Max. 2.5 cps.

223.2/126.1404.1/372.1

378.0/159.1192.2/160.2

213.1/72.2292.2/70.2

174.1/104.1406.2/251.1

276.2/244.1233.1/72.1

364.1/152.2461.1/158.2

312.2/267.2207.2/72.1

511.1/158.1280.2/220.2

222.1/165.2163.1/88.1

215.1/187.2199.2/72.2

282.2/212.1208.2/109.1

212.2/170.1252.3/91.1

242.2/186.1

Q1/Q3 Masses, Da

0.0

0.5

1.0

1.5

2.0

2.5

In

te

ns

it

y,

c

ps

Scheduled MRM™ Algorithm

XIC of + MR M (158 pairs ): 237.0/194.2 am u Expected RT: 6.2 ID: Ca rbam azepine 1 fro m Sam ple 20 (1 n g/... M ax. 6.8e6 cps .

1.0 2 .0 3.0 4.0 5.0 6.0 7 .0 8.0 9.0 10.0

Tim e, m in

0.0

5.0e5

1.0e6

1.5e6

2.0e6

2.5e6

3.0e6

3.5e6

4.0e6

4.5e6

5.0e6

5.5e6

6.0e6

6.5e6

7.0e6

7.5e6

8.0e6

Inte

ns

ity, c

ps

6.2

Detection of

158 MRM transitions

Figure 2. LC-MS/MS Detection of 80 PPCP at 1 μg/L

XIC of +MRM (158 pairs): 237.0/194.2 amu Expected RT: 6.2 ID: Carbamazepine 1 from Sample 35 (Agric... Max. 1.4e5 cps.

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0Time, min

0.0

5000.0

1.0e4

1.5e4

2.0e4

2.5e4

3.0e4

3.5e4

4.0e4

4.5e4

5.0e4

5.5e4

6.0e4

6.5e4

7.0e4

7.5e4

8.0e4

8.5e4

9.0e4

9.5e4

1.0e5

1.1e5

1.1e5

1.2e5

1.2e5

1.3e5

1.3e5

1.4e5

Inte

ns

ity

, c

ps

6.2

36ppt Atrazine

10ppt Carbamazepine

3ppt Cotinine3ppt Diuron

5ppt Metolachlor3ppt Simazine

1ppt

Sulfadimethoxazine

A

XIC of +MRM (158 pairs): 216.1/96.1 amu Expected RT: 6.9 ID: Atrazine 2 from Sample 36 (Urban) of 200... Max. 1.9e4 cps.

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0Time, min

0.00

5000.00

1.00e4

1.50e4

2.00e4

2.50e4

3.00e4

3.50e4

4.00e4

4.50e4

5.00e4

5.50e4

6.00e4

6.50e4

7.00e4

7.50e4

8.00e4

8.50e4

9.00e4

9.50e4

1.00e5

1.05e5

1.10e5

1.15e5

Inte

ns

ity

, c

ps

6.9

7.26.96.6

C

10ppt Atrazine

200ppt Cotinine

5ppt Metolachlor

2ppt Simazine1ppt

Thiabendazole

420ppt Caffeine

Figure 3. Quantitation of PPCP in a water

samples from an agricultural area

Figure 4. Quantitation of PPCP in a water

samples from an urban area

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

0.090

0.100

Drinking water RiversLakesCreeks

100 ng/L

?

Wie

n

Rio de Janeiro

LOQ

Sea

Holy water from a church in Roma

0.000

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

Phila

delp

hia

Drinking water

Rivers

Lakes

Creeks Sea

100 n

g/L

Rom

a

?

Wie

n

St.

Pete

Beach

Rio

de J

aneiro

Dre

sden

Mountain hut in Switzerland

Zürich

Vienna

Berlin

0.000

0.050

0.100

0.150

0.200

0.250

0.300

?

Phila

delp

hia

Sa

nta

Fe

To

ron

to

Wie

n

St.

Pe

te B

ea

ch

Rio

de

Ja

ne

iro

Sca

rbo

roug

h

Ro

ma

Drinking water

Rivers

LakesCreeks

Sea

ZermattCanoe

Zürich

10

0 n

g/L

Berlin

Figure 6. Findings of Benzoylecgonine, a

metabolite of cocaine and indicative for cocaine

abuse, in various water samples

Figure 7. Findings of caffeine in various water

samples

Figure 8. Findings of the virility regulator

Sildenafil in various water samples

Compound µg/L LOQ (µg/L)

Acetaminophen 9.1 0.010

Benzoylecgonine 0.47 0.001

Caffeine 38 0.010

Carbamazepine 0.21 0.001

Codeine 0.050 0.001

Dextromethorphan 0.021 0.001

Diazepam 0.003 0.001

EDDP 0.001 0.001

Erythromycin 1.7 0.050

Morphine 0.15 0.005

Sildenafil 0.015 0.005

Thiabendazole 0.016 0.001

Table 1. Compounds detected in a holy water sample

Figure 9. Comparison of two water sample using the MasterView™: DEET was identified

using automatic MS/MS library searching (FIT = 92.2)

Samples from wilderness areas

with extensive recreational use

Increasing urbanity of samples

Drinking water produced from a

river bank filtrate during flood

DEET

Figure 11. Principal Components Analysis to identify unexpected water contaminants: the insect

repellent DEET was identified in samples from wilderness areas and DEET profile measured in

Algonquin Provincial Park (ON)

23

6

5

1

4

8

18

2

3

4

5

6

7

Lake water (urban) “Control” water

Formula C12H12NO

MS/MS

Figure 10. Further identification of DEET based on empirical formula calculation followed by

ChemSpider searching