5ppt metolachlor 6 thiabendazole deet 9 “control” water poster a0_fera.pdf · the water sample...
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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