Evaluating a Tap Water Contamination Incident Attributed to Oil

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Evaluating a tap water contamination incident attributed to oil contamination by non-targeted screening strategies Beili Wang, Yi Wan, Guomao Zheng, and Jianying Hu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b05755 • Publication Date (Web): 10 Feb 2016 Downloaded from http://pubs.acs.org on February 13, 2016

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Environmental Science & Technology

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Evaluating a tap water contamination incident attributed to oil contamination by

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non-targeted screening strategies

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Beili Wang, Yi Wan*, Guomao Zheng, Jianying Hu

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Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences,

Peking University, Beijing 100871, China (Received

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*Address for Correspondence:

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Address for Correspondence

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Dr. Yi WAN

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College of Urban and Environmental Sciences

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Peking University

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Beijing 100871, China

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TEL & FAX: 86-10-62759126

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Email: [email protected]

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ACS Paragon Plus Environment


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ABSTRACT

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The present study applied non-targeted screening techniques as a novel approach to

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evaluate the tap water samples collected during the “4.11” tap water pollution incident

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occurred on April 11, 2014 in Lanzhou in west China. Multivariate analysis (PCA and

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OPLS-DA) of about 3000 chemical features obtained in extracts of tap water samples by

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ultrahigh-pressure liquid chromatography quadrupole time-of-flight mass spectrometry

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(UPLC-QTOF-MS) analysis showed significantly different chemical profiles in tap water

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from pollution regions versus reference regions during the event. These different chemical

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profiles in samples from different regions were not observed in samples collected during the

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non-pollution period. The compounds responsible for the differences in profiles between

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regions were identified as naphthenic acids (NAs) and oxidized NAs (oxy-NAs) after the

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sample extracts underwent bromination to explore saturations, dansylation to identify

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hydroxylations and corresponding MS/MS mode analysis. A consistent finding was further

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observed in the targeted analysis of NA mixtures, demonstrating that the Lanzhou “4.11” tap

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water pollution incident could be attributed to oil spill pollution, and NA mixtures would be a

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marker for oil contamination. Such evaluations can help to rapidly discriminate pollution

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sources in accidental pollution events and contribute to regular water monitoring management

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of water safety issues.

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Keywords: Water safety, tap water, accidental pollution events, metabolomics, naphthenic

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acids

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Introduction

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Recent decades have witnessed a rise in the environmental awareness of water safety,

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which is one of the national issues holding the greatest socio-economic threats to national

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security.1-3 The accidental release of pollutants into the environment can result in the loss of

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life and property and severely affect the safety of water, especially in countries undergoing

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rapid industrial and urban development.1, 4, 5 The number of water pollution accidents was

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reported to be 6677 from 2000 to 2008 in China according to the Ministry of Environmental

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Protection (MEP), and many of these events have threatened the public health of local

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communities.6-7 It is therefore urgent to strengthen the capabilities to monitor and properly

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judge events of accidental pollution.

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Target screening is currently the main strategy to identify and quantify pollutants for

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which standards are available.8-10 To provide maximum selectivity and sensitivity, only

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characteristic ions or ion transitions of targeted analytes are monitored in target ion

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monitoring.3, 11-13 However, no preliminary information concerning responsible pollutants was

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known when the accidents occurred in most pollution events.6-7 The target screening methods

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were specifically developed for a certain group of substances, and would miss the compounds

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that are not selected at the start of the analyses. For example, unusual odors were reported in

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river water and tap water on May 10, 2014 in Jingjiang city, China, and the city had to stop

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the supply of tap water for seven hours, but the causes of the accident were still not clear even

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the Chinese MEP continuously monitored the water samples in 62 locations along the river

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for 32 hours. In contrast, the tentative non-targeted techniques are superior for screening of

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unknown compounds.14-16 The non-targeted screening is substantially harmonized by 3



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researchers from 18 institutes from 12 European countries recently due to its ability for

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identification of a wider range of compounds in water samples.17 However, identifying the

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major functional groups of unknown compounds based on the high resolution data combined

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with limit fragmentations is a major challenge for clarifying the structures of the non-targeted

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mass.15,

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derivatization regents would specifically react with different functional groups, for example,

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dansyl chloride could selectively react with hydroxyl groups and generate a collisional

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fragmentation of dansyl moiety.18-20 Thus derivatizations combined with MS/MS analysis

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could help identify the structure of responsible pollutants in the sample extracts.

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More suitable for this purpose is chemical derivatizations, since some

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To test the hypothesis about the advantage of non-targeted screening in water analysis,

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the proposed non-targeted techniques were applied to evaluate a tap water contamination

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incident. On April 11, 2014, the so-called “4.11” tap water pollution incident occurred in

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Lanzhou, Gansu province, China, during which the concentrations of benzene (up to 200 µg/L)

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in the city’s tap water rose to 20 times above the national limit according to the city’s

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environmental protection office. More than 800 metric tons of polluted water were supplied

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before the source was found. We screened all the non-targeted mass in tap water samples

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collected during the events and a non-pollution period. The chemical profiles were obtained

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from sufficiently accurate mass measurements, and thousands of resulting chemical formulae

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underwent multivariate analysis to identify the responsible pollutants. Chemical

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derivatizations including bromination and dansylation were applied to identify the saturation

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or hydroxylation of the responsible pollutants. The structures of the identified pollutants were

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finally clarified together with a targeted analysis to reveal the pollution sources in the event. 4


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Materials and methods

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Sample collection

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On April 11, 2014, concentrations of benzene were reported to be more than 20 times

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above the national limits after comprehensive target screening of the water samples following

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the national guidelines for drinking water quality by the local environmental protection office.

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During the “4.11” tap water pollution incident, Xigu (XG) and Anning (AN) districts were

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reported to the worst-hit areas according to the city’s environmental protection office, and

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Honggu (HG), Qilihe (QLH) and Chengguan (CG) districts were the less affected area

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possibly due to different distances to the location of the pollution sources. On the same day of

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the accident, 14 tap water samples were collected from the heavily contaminated XG and AN

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districts, respectively, and 15 tap water samples were taken from three other districts of

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Lanzhou city including HG, QLH and CG districts (Figure 1). Six months after the accident

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(October 10, 2014), tap water samples were collected again from the same locations to allow

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comparisons of the pollutants in the water samples collected at the time of the accident with

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those in the non-pollution period. Water samples were collected from the kitchen tap and

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allowed to flow from the tap without an aerator for about 3 min prior to completely filling the

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sample bottle with no headspace. All water samples were collected in 500 mL amber glass

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bottles, which were washed by methanol and pure water before use.

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Sample preparation

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Water samples were stored with ice during transportation and extracted within 6 h in the

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local laboratory after being filtered by a glass microfiber filter GF/C 1.2 µm (Whatman,

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Maidstone, UK). The details of chemicals and reagents are provided in the Supplementary 5



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Data. 500 mL of water spiked with 0.1 µg of surrogate standards (12-oxochenodeoxycholic

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acid and 1-pyrenebutyric acid) was extracted on a SPE MAX cartridge (Oasis MAX, 6 mL,

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150 mg, Waters, USA), of which the sorbent is synthesized from the reversed-phase Oasis

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HLB copolymer and features two retention mechanisms: anion exchange and reversed phase.

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This makes the cartridge suitable for extractions of both neutral and charged compounds in

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water samples. The cartridges were preconditioned by 6 mL of methanol and 6 mL of pure

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water, and then rinsed with 6 mL of 5% ammonia. After dried under a flow of nitrogen, the

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MAX cartridge was eluted with 12 mL ethyl acetate saturated with hydrochloric acid (2M

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HCl:ethyl acetate =1:10, v/v). The elute was washed with pure water for three times and

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reconstituted with 100 µL of methanol for analysis by an ultrahigh-pressure liquid

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chromatography (UPLC) coupled to a quadrupole time-of-ﬂight mass spectrometer

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(QTOF-MS) with electrospray ionization in negative ionization mode (ESI-).

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Non-targeted UPLC-QTOF-MS analysis

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Non-targeted chemical profiling LC-MS analysis was carried out on a Waters ACQUITY

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UPLC coupled to a Xevo QTOF-MS (G2, Waters). An ACQUITY UPLC BEH C18 column

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(2.1×100 mm, 1.7 µm particle size) and a mobile phase consisting of (A) ultrapure water

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containing 10 mM ammonium acetate and (B) methanol were used for chromatographic

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separation, with a flow rate of 0.2 mL min-1, to obtain the abundant responses of all the

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potential chemicals in water samples. The column was maintained at 40°C, and the injection

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volume was 3 µL. Mass spectra were collected in full-scan from m/z 80 to 1000. Spectral

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peaks were deconvoluted and aligned using Waters MarkerLynx (version 4., Waters

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Corporation, Milford, MA) with the following parameters: data collection parameters were set 6


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as intensity threshold 500 counts, mass window level at 50-1000 Da, retention time window

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of 16 min, and noise elimination level at 6.00. The data sets (spectral peak areas of compound

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divided by sum area of two surrogate standards’ spectral peak) were normalized to total

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spectral area for each sample, and exported to SIMCA-P+ (ver. 13.0; Umetrics) for

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multivariate statistical analysis.

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Markers identification (non-targeted analysis)

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The identities of discriminatory chemicals were determined by their accurate mass

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composition and from fragmentation data, which were obtained from collision-induced

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dissociation (CID) using QTOF-MS/MS analysis with electrospray ionization in negative

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ionization mode (ESI-). MS/MS mode was applied to acquire fragmentation data by manually

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setting the m/z values of precursor ions, which are used to derive the structural information

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about these molecules in combination of precursor m/z and retention time. To eliminate

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compounds with carbon-carbon double bonds, the residues were redissolved in 2 mL of CCl4

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and reacted with 8 mL of 1% (v/v) bromine in CCl4.21-22 Excess bromine was removed by

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reacting with 100 µL of 2-pentene, and redissolved in 100 µL of acetonitrile after drying by a

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gentle nitrogen. The extracts were further derivatized with dansyl chloride (DNS) to identify

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the chemical nature of alcohol group according to the procedure reported previously (2013).23

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Briefly, the extract were added with 0.2 mL pyridine and a mixture (0.2 mL) of 30 mg/mL

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DNS and 30 mg/mL catalyst (4-dimethylamiopryidine) dissolved in DCM. The mixture was

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shaken with a vortex device for 1 min and incubated at 65°C for 60 min. The residuals were

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blown to dryness and then dissolved with 0.1 mL of acetonitrile for UPLC-QTOF-MS/MS

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analysis. UPLC-QTOF-MS/MS analysis was used to characterize the fragmentation pattern of 7



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each maker feature of interest (as determined by univariate and multivariate techniques)

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according to the fragmentation ions reported in our previous study.23-24 MS/MS was carried

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out on the Waters QTOF system described above with a 10 - 30V collision energy ramp and a

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50 - 1000 Da mass range.

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Targeted analysis

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To identify and quantify the groups of identified markers, water samples were measured

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using the same LC-MS method with that in non-targeted analysis. To correct for variation

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between batches in the targeted analysis, quality assurance and quality control (QA/QC) was

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applied to each chemical concentration value. Briefly, all equipment were rinsed with acetone

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and hexane to avoid sample contamination. A procedural blank was incorporated in the

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analytical procedures for every batch of 10 samples, and the total amount of NAs and

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oxy-NAs in field blank samples were less than 3.5±0.4 µg. The efficiencies of the sample

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preparation procedure was assessed by analyzing water samples collected from each district

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spiked with standard solutions of model NAs and oxy-NAs, of which the detail information

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was provided in SI Table S1. The absolute recoveries of model NA and oxy-NA compounds

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were 90±25%, 92±19%, 82±18%, 88±24% and 99±29% in spiked water samples collected

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from XG, CG, QLH, AN and HG, respectively (n=15), of which the recoveries (82-99%)

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were in the acceptable range for semi-quantifications of NA mixtures and the relatively high

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deviations were possible due to the poor quantification of TOFMS. Surrogate standards

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(1-pyrenebutyric acid and 12-oxochenodeoxycholic acid) were spiked to samples prior to

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extraction to compensate for the loss of target compounds during the extraction process and

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correct the variation of instrument response and matrix effect. The efficiencies of the sample 8


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preparation procedure were assessed by analyzing water samples collected from each district

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spiked with surrogate standard solutions. Recoveries of 1-pyrenebutyric acid and

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12-oxochenodeoxycholic acid were 70±25% and 75±29% (n=29) in all analyzed water

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samples, respectively. The MDLs were 1.2-27 ng/L and 0.05-0.35 ng/L for NAs and oxy-NAs

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in water samples, respectively. The detail analytical information about targeted analysis of

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PAHs and alkyl-PAHs were provided in Supporting Information (SI).

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Statistical analysis

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Normalized peak area matrices were exported to SIMCA-P 13.0 (Umetrics, Umea,

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Sweden) for multivariate statistical analysis. Spectral regions containing blank water and

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methanol signals were excluded from the analysis. Clustering of the samples was assessed

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using principal component analysis (PCA) to reveal the differences among sampling districts.

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Orthogonal partial least-squares-discriminant analysis (OPLS-DA) was carried out to generate

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the maximum separation between the classes of reference regions versus pollution regions.

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Each OPLS-DA model was evaluated by both the internal permutation test and external

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validation test. Unpaired Student's t-tests were employed to ensure that the polluted markers

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extracted with holistic OPLS-DA analysis were significantly differentially expressed between

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the polluted and control groups. P value threshold of 0.05 was used to define the significance.

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Results and discussion

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Changes in chemical profiles of the tap water

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The evolution of HRMS coupled with ultrahigh-pressure liquid chromatography (UPLC)

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with superior sensitivity and selectivity has opened up new windows of opportunity for 9



and

identifying

the

compounds.25-29

unknown

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profiling

Using

the

non-targeted

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UPLC-QTOF-MS method, 3391 chemical features were extracted for each water sample.

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PCA score plots were used to determine whether the chemical profile in the water samples

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from each district were sufficiently unique to distinguish the different polluted regions. As

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shown in Figure 2a, clear separations were observed between the districts of XG and AN and

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those of HG, CG and QLH. This is consistent with the report by the city’s environmental

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protection office that XG and AN districts were the most polluted areas in the “4.11”

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incident.30 In comparison, the PCA score plots illustrated no significant differences among the

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five districts for the water samples collected during the non-pollution period (Figure 2b),

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suggesting that the chemical profiles of the tap water of the five districts in Lanzhou were

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generally similar. The results demonstrated that the unknown pollution sources caused

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significantly different chemical profiles in the tap water from XG and AN districts compared

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with the other districts during this event, and the profile would provide important information

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for clarifying the potential pollution sources.

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Non-targeted analysis of PCA of the five districts showed significant differences

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between the XG and AN districts and the QLH, CG and HG districts during the pollution

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period. Therefore, we considered XG and AN as the pollution region and QLH, CG and HG

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districts as the reference region. To investigate the key pollutants in tap water from the

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polluted region, the variances of chemical profiles between the pollution region and reference

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region were examined by supervised multivariate OPLS-DA. The OPLS-DA scatter plots

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showed obvious clustering of tap water samples from the two regions during the event (R2

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(cum) = 0.993, Q2 (cum) = 0.943, Figure 2c), whereas the OPLS-DA model for samples 10


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collected in the non-polluted period showed poor predictive ability (R2 (cum) = 0.789, Q2

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(cum) = -0.327, Figure 2d). This is consistent with the PCA results that samples from the

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pollution and reference regions can only be separated during the “4.11” tap water pollution

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incident. The quality of the OPLS-DA model was further evaluated by a permutation test

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(with 100 iterations) performed in the corresponding PLS-DA model. The Q2 and R2 values in

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the permutation test for the modeling of samples collected during the event were higher than

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the original points, and the regression of the Q2 points intersected the vertical axis below zero

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(SI Figure S1), strongly indicating that the OPLS-DA model was statistically valid. However,

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the permutation test for the modeling of samples collected during the non-pollution period

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showed that the OPLS-DA model was not valid (SI Figure S1), which is consistent with the

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poor predictive ability of the model. To select statistically significant chemicals related to the

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differences between regions during the event, an S-plot was constructed on the basis of the

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validated OPLS-DA model (SI Figure S2). Variables that are at the edges of the sigmoidal

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curve can be considered as suitable targets for investigation as markers of exposure, and about

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78 peaks responsible for the separation were considered as potential markers for clarifying the

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pollution sources of the accident.

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The non-targeted screening via high resolution MS has gained increasing importance for

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monitoring unknown organic trace substances in water resources.31 Generally, significantly

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different chemical profiles were observed among different types of water such as drinking

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water, ground water, wastewater etc.15, 31 In the present study, distinct chemical profiles were

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firstly observed in tap water samples from different regions in the water pollution incident,

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and the difference disappeared during the non-pollution period. In most pollution events, no 11



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preliminary information concerning responsible pollutants was known when the accidents

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occurred. The traditional target analytical methods were specifically developed for a certain

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group of substances, and would miss the compounds that are not selected at the start of the

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analyses. In comparison, the non-targeted screening technique could screen all possible

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chemicals in the sample extracts and identify the responsible pollutants based on comparisons

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between polluted and reference samples. For examples, a suite of polar petroleum makers

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were identified for indicating the oil spill source based on the PCA analysis of elemental

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composition data in oils of fuel tanks and environmental field samples.32 In the present study,

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the non-targeted screening strategies is based on the assumption that no preliminary

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information concerning the pollution sources was known, and the method provided a new

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comprehensive monitoring approach of organic trace substances for source clarifications

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and/or early warning in accidental pollution events.

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Identification of potential markers

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The identification of the responsible pollutants for the regional differences is a great

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challenge due to the lack of comprehensive spectral libraries for soft ionization techniques in

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contrast with the standards or public databases of metabolites such as Metlin, HMDB, and

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KEGG.15, 17 The structures of the non-targeted mass could not be confirmed only based on the

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high resolution data combined with results of compound database queries (e.g., ChemSpider),

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and identifying the major functional groups of unknown compounds is even more difficult. In

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the present study, potential markers were identified mainly through MS fragmentations and

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chemical derivatization of the compounds. The molecular formula of the compounds can be

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roughly determined by the accurate molecular weight by HRMS and MS/MS spectra, and 12


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chemical derivatizations including bromination and dansylation were applied to explore the

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saturation or hydroxylation of the pollutants.

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The 78 ions of interest were firstly lessened to about 52 ions by Student’s t-test and then

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identified from their accurate mass composition and elemental composition. Many structures

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were present in electrospray ionization (ESI) as the [M−H]- ion, and a number of the ions

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were found to be compounds with the molecular formula of CnH2n+ZOx (e.g. C16H32O2,

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C18H36O2, C12H24O2, C18H34O2, C13H20O4 C15H22O4, C14H20O5 and C15H22O5), of which the

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intensities in samples from the pollution region were, on average, 29-fold higher than those in

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samples from the reference region in the event. Bromination was first conducted to eliminate

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compounds with carbon-carbon double bonds, and the variations of peak intensities of the

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CnH2n+ZOx ions between non-brominated and brominated samples ranged from -5.2% to 1.8%,

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suggesting that these compounds contained cyclic structures. The brominated extracts were

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further characterized in the QTOF-MS/MS analysis to investigate their ESI mass spectral

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fragmentation pathways. As shown in Figure 3 and Table 1, molecular ions ([M−H]−) and

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[M−H−H2O]− were observed for C16H32O2, C18H36O2, C12H24O2 and C18H34O2, suggesting that

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these compounds were saturated acids or ring-containing acids (naphthenic acids (NAs)). Two

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more fragment ions of [M−H−CO2]− and [M−H−H2O−CO2]− were found for the ions of the

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C13H20O4, C15H22O4, C14H20O5 and C15H22O5 species (Figure 3 and Table 1). As shown in SI

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Figure S4, neutral loss of CO2 and H2O moieties were the characteristic mass fragmentations

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for mono-oxidized acids (12-oxochenodeoxycholic acid and 12-hydroxystearic acid), and four

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diagnostic fragments were generated by the loss of more H2O or both CO2 and H2O moieties

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for 12-oxochenodeoxycholic acid. Thus, neutral losses of the CO2 and H2O moieties were the 13



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characteristic mass fragmentations for oxidized NAs (oxy-NAs), which is similar to the

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fragmentation ions of commercial NAs by ESI--MS/MS analysis reported previously.24, 33-36

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The loss of H2O, CO2 and H2CO3 moieties in CnH2n+ZO4 and CnH2n+ZO5 indicated that these

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compounds were hydroxylated NAs or ketonic NAs, not compounds with ester or di-acid

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groups. Moreover, the retention times of C13H20O4, C15H22O4, C14H20O5 and C15H22O5 were

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earlier than those of the non-oxidized acids (Table 1), which is consistent with the elution

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sequence of the oxidized acid and non-oxidized acids reported previously.23-24 The above

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results indicated that these ions were possibly NAs and/or oxy-NAs, both soluble components

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of petroleum.

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To further identify the chemical nature of the oxy-NAs (hydroxyl or ketone) in the

295

extracts, a derivatization method with DNS was applied. DNS is only reactive with hydroxyl

296

groups in optimized derivatization conditions, and derivatized compounds were analyzed in

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positive ion mode, which excluded the interferences of acids in the sample extracts. As shown

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in SI Figure S5, ionization and fragmentation of the dansyl derivatives of hydroxylated NA

299

standards resulted in protonated molecular ions [M+H]+ and produced product ions at m/z

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252.0694 and m/z 171.1048. The ion m/z 252.0694 is the protonated molecular ion of DNS,

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and the ion m/z 171.1048 originates from a cleavage of a C-S bond in the dansyl portion of

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the molecule (SI Figure S5). Figure 3 and Table 1 show the MS/MS fragment ions of dansyl

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derivatives from the corresponding oxy-NAs in the extracts of tap water from the pollution

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region in the event. The major dansyl derivative parent and product ions, not observed before

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derivatization, were similar to the fragmentation pattern of hydroxylated NA standards. While

306

the reference standards were not commercially available for these identified compounds, the 14


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fragmentation patterns of the compounds were same with those of standards of model

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oxy-NAs before or after the derivatization. The results demonstrated that the C13H20O4,

309

C15H22O4, C14H20O5 and C15H22O5 detected in the sample extracts were mainly composed of

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hydroxylated NAs. This study suggests that saturated fatty acids, NAs and hydroxylated NAs

311

would be the key pollutants for regional discrimination in the event.

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Targeted analysis of naphthenic acids

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NA mixtures are a group of chemicals with formula of CnH2n+ZOx (x=3, 4, 5), where “n”

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is the carbon number and “Z” refers to the cylinder number. “Z” is zero or a negative, even

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integer that specifies the hydrogen deficiency resulting from ring formation.37-39 NA mixtures

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are the primary toxic components in oil sands process-affected waters produced at the oil

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sands extraction plants in northeastern Alberta, Canada.40 Previous studies have shown that

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NA mixtures are potential indicators for oil contamination in the aquatic environment because

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that they are polar and soluble components of petroleum and persist in the environment.41 The

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detection of NA mixtures in the polluted tap water is consistent with the suspected cause of

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the event, i.e., that an oil pipeline near the water plant leaked, which led to the concentration

322

of benzene exceeding the standard. The concentrations of the NA mixtures were generally

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semi-quantified based on integration of the hump peak of the NA congeners assuming that the

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responses for individual NA isomers in the hump peaks were similar because the separation

325

method and standards for all of the individual NAs were not available.42-45 The peak intensity

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of the NAs and oxy-NAs could not be automatically integrated by MakerLynx due to the

327

characteristic “humps” of the NA mixtures. Therefore, about 150 NA congeners were

15



328

manually integrated and semi-quantified with the method reported previously for further

329

multivariate statistical analysis.23

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A consistent finding was observed in the non-targeted and targeted analysis. In the

331

OPLS-DA score plots, tap water samples collected from the pollution region during the event

332

could be clearly separated from those collected from the reference regions (R2 (cum) = 0.89,

333

Q2 (cum) = 0.508), but no obvious separation was observed for samples collected during the

334

non-pollution period (R2 (cum) = 0.584, Q2 (cum) = 0.0871) (SI Figure S6). The permutation

335

test also showed that only the OPLS-DA model for samples collected during the event was

336

statistically valid (SI Figure S7). These results demonstrated that the NA mixture profile from

337

the UPLC-QTOF-MS analysis consistently produced clearly defined groupings and a highly

338

valid model in OPLS-DA analysis for tap water samples collected during the event. Figure 4

339

shows the boxplots of the concentrations of detected NAs and oxy-NAs in tap water samples

340

from the pollution and reference regions during the event. Concentrations of CnH2n+ZOx with

341

Z values of -8 to 0 and x of 2 to 5 in water samples from the pollution region were statistically

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significantly higher than those in samples from the reference region. Especially for ions of

343

267.2324 and 281.2481 (one ring) and 219.139, 401.342, 171.1021 and 183.1021 (four rings

344

and one hydroxyl), concentrations were below detection limits in samples from the reference

345

regions but relatively high in samples from the pollution region (Figure 4). The results

346

together with the non-targeted OPLS-DA analysis confirmed that the “4.11” tap water

347

pollution incident in Lanzhou could be attributed to oil spill pollution. A previous study also

348

screened elemental compositions of thousands features by high resolution FT-ICR MS in two

349

tanks of heavy fuel oils and field oil from a rocky shoreline along San Francisco Bay 16


Page 16 of 29

Page 17 of 29


350

following the M/V Cosco Busan oil spil.32 While O2 species (possible NAs) were identified

351

without any structure information and oxygenated species of NAs (possible oxy-NAs) would

352

have to be removed by solid phase extraction prior to instrument analysis, the abundance

353

variations of NAs suggested that future studies should investigate the use of NA mixtures as a

354

potential source-specific fingerprint for identification purpose. The present study identify O2

355

species and oxygenated products as the responsible pollutants for the differences in profiles

356

between regions during the “4.11” tap water pollution incident in Lanzhou, and clarified these

357

feature to be NAs and oxy-NAs based on the chemical derivatization and MS/MS analysis.

358

The results is consistent with the hypothesis of Corilo’s study,32 and demonstrated that NA

359

mixtures would be a source-specific marker for oil contamination.

360

Numerous studies have shown that NA mixtures were toxic to aquatic organisms such as

361

yellow perch embryos, Japanese Medaka, larval amphibian etc, limited studies is available

362

about effects of NAs on mammalian health and development.46-47 Hepatotoxicity as an acute

363

effect was observed in the rat feed with aqueous solutions containing 550.8 mg/L NAs.47 And

364

expression of cardiac specific markers were significantly up-regulated in mouse embryonic

365

stem cells exposed to NAs at concentrations of 25 µg/L.46 The total concentrations of detected

366

NA isomers ranged from 0.08 to 0.7 µg/L in tap water samples from polluted regions in

367

Lanzhou (Figure 4), which were much less than the effect concentrations of NAs in either rat

368

or mouse stem cells, suggesting the low risks of dietary uptake of the contaminants in the

369

accidental pollution event.

370

Targeted analysis of PAHs and alkyl-PAHs were conducted in the water samples, since

371

residues of PAH mixtures are common proxies to detect oil pollution, oil weathering, and 17



372

source identification at the oil spill impacted areas.48-51 As showed in SI Table S2, the total

373

concentrations of detected PAHs and alkyl-PAHs were in the range of 10.6-28.7 ng/L and

374

2.8-15.7 ng/L in water samples collected during the pollution and non-pollution periods,

375

respectively. Significantly high concentrations of PAH mixtures observed in water samples in

376

the pollution events compared with those collected during the non-pollution period suggested

377

that oil contamination might be the cause, which is consistent with the results of NA

378

investigations. However, no significant spatial differences could be found for PAH mixtures

379

during the events, and this could be due to that PAH mixtures have many other sources such

380

as forest fires, agricultural burning, coal, and bacteria biosynthesis etc. Thus, compared with

381

suspected target analysis, non-targeted screening would help found the responsible pollutants,

382

which is specifically related to the pollution sources, and contribute to rapidly discriminate

383

pollution sources in accidental pollution events.

384

While benzene is a natural part of crude oil, the presences of benzene in environment is

385

mostly related to the effluent discharges of chemistry industrial, since the greatest use of

386

benzene is as a building block for making plastics, rubber, resins and synthetic fabrics.52 Thus

387

the pollution sources were not clear when benzene, one of the regular monitoring chemicals in

388

tap water, was detected with concentrations about 20 times above the national limit in

389

Lanzhou. It took a long time for the local agency to find an oil leaking site close to the

390

drinking water pipe, but no direct evidences between water samples and pollution sources can

391

be provided based on the government report. In the present study, the detection of NA

392

mixtures, identified by a non-targeted analytical strategy and chemical derivatization,

393

demonstrated the an oil leaking as the major cause of the event because NAs have unique 18


Page 18 of 29

Page 19 of 29


394

sources for oil contamination, providing direct evidence between water samples and pollution

395

sources. The non-targeted screening by high-resolution MS could potentially help to rapidly

396

discriminate the regional difference between pollution and reference areas and determine the

397

actual pollutants responsible for pollution accidents.

398 399 400 401

Acknowledgments The research is supported by National Natural Science Foundation of China (21177003, 21422701), and National Basic Research Program of China (2015CB458900).

402 403 404

Supplementary Data Text, figures, and tables addressing (1) chemicals and reagents; (2) sample analysis of

405

PAH mixtures; (3) validation plots from permutation tests for non-targeted OPLS-DA analysis;

406

(4) S-plots of OPLS-DA of water samples from polluted and reference regions during the

407

event; (5) Proposed structures of some oxy-NAs; (6) MS/MS spectra of oxy-NAs; (7) MS/MS

408

spectra of oxy-NAs derivartized with DNS; (8) the scores plots of OPLS-DA model for

409

targeted analysis of NAs in water samples during the events and non-pollution period; (9)

410

validation plots obtained from permutation tests for targeted OPLS-DA analysis; (10)

411

structure information of model NA compounds; (11) levels of PAHs and alkyl-PAHs detected

412

in all water samples.

19



413

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577 578 579 580

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Page 24 of 29

Table 1. Precursors and MS/MS fragment ions of NAs, and oxy-NAs and their corresponding derivatives with dansyl chloride generated in MS/MS mode of QTOF-MS in extracts of tap water, and the MS/MS spectra with precursor ions of 255, 239 and 281 as shown in Figure 3. MS/MS Compounds

Precursor ion

RT (min)

C18H34O2

281

13.00

C18H36O2

283

13.54

C16H32O2

255

11.20

C12H24O2

199

10.01

C13H20O4

239

4.38

C15H22O4

265

4.57

C14H20O5

267

4.38

C15H22O5

281

4.44

NAs

Oxy-N As

MS/MS of dansyl chloride derivatives

Mass fragment ions

Mass fragment ions

[M-H-CO2]-

[M-H-H2 O-CO2]-

[M+DNS+ H]+

[C12H14NO3S]+

[C12H13N]+

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

221.1188

195.139

177.1283

474.1981

252.0676

171.1044

(-2.1 ppm)

(4.5 ppm)

(2.6 ppm)

(2.3 ppm)

(6.5 ppm)

(-7.1 ppm)

(-2.3 ppm)

265.1437

247.1329

221.1539

203.1432

500.2127

252.0698

171.1039

(-1.1 ppm)

(-2.0 ppm)

(-1.3 ppm)

(-2.0 ppm)

(4.0 ppm)

(1.6 ppm)

(-5.3 ppm)

267.1218

249.1114

223.1316

205.1231

502.1918

252.0684

171.1045

(-5.2 ppm)

(-5.2 ppm)

(-8.1 ppm)

(1.0 ppm)

(3.8 ppm)

(-4.0 ppm)

(-1.8 ppm)

281.1388

263.1289

237.1481

219.1398

516.2032

252.0691

171.104

(-0.4 ppm)

(2.8 ppm)

(-4.2 ppm)

(5.9 ppm)

(-4.6 ppm)

(-1.2 ppm)

(-4.7 ppm)

[M-H]-

[M-H-H2O]-

281.2479

263.2384

(-0.7 ppm)

(3.4 ppm)

283.2635

265.2526

(-0.7 ppm)

(-1.9 ppm)

255.2332

237.222

(3.1 ppm)

(0.8 ppm)

199.1694

181.16

(-2.0 ppm)

(4.4 ppm)

239.1278

Mass errors of fragmentation ions higher than 5 ppm are possible due to the low abundance of the compounds.

24


Page 25 of 29


581 582

Figure 1.Sampling sites of tap water collected in Lanzhou, China.

583 584

25



585 586 587 588 589

Figure 2. Multivariate analysis of untargeted profiling of tap water extracts. Classes of PCA score plots and OPLS-DA score plots are from the polluted period (a, c) and the non-pollution period (b, d), respectively.

590

26


Page 26 of 29

Page 27 of 29

591 592 593 594 595 596


Figure 3. MS/MS spectra of NAs with precursor ions of 255 (a) and 281 (b), oxy-NAs with precursor ions of 239 (c) and 281 (d) and corresponding oxy-NAs derivatives with precursor ions of 474 (e) and 516 (f) in the extracts of tap water. The proposed structures of e and f were shown in SI Figure S3.

597

27



598 599 600 601 602 603

Figure 4. Boxplots of typical identified markers (CnH2n+ZOx with Z of -8 to 0 and x of 2 to 5) in the tap water from the reference region (R) and polluted region (P) during the events. All of the compounds exhibited statistically significantly higher levels in water samples from the polluted region (p

Evaluating a Tap Water Contamination Incident Attributed to Oil

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