Non-target screening and time-trend analysis of sewage sludge

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Environmental Measurements Methods

Non-target screening and time-trend analysis of sewage sludge contaminants via two-dimensional gas chromatography – high resolution mass spectrometry Cathrin Veenaas, Anders Bignert, Per Liljelind, and Peter Haglund Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01126 • Publication Date (Web): 14 Jun 2018 Downloaded from http://pubs.acs.org on July 2, 2018

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Environmental Science & Technology

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Non-target

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contaminants via two-dimensional gas chromatography – high resolution

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mass spectrometry

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Cathrin Veenaas, *,† Anders Bignert‡, Per Liljelind, † Peter Haglund†

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†

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‡

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Stockholm, Sweden

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Corresponding author: Cathrin Veenaas, [email protected], +46 90 786 5171

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___________________________________________________________________________

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screening

and

time-trend

analysis

of

sewage

sludge

Umeå University, SE-90187 Umeå, Sweden Contaminant Research Group, Swedish Museum of Natural History, PO Box 50 007, SE-10405

TOC art

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1 ACS Paragon Plus Environment


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Abstract

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Non-destructive sample clean-up and comprehensive two-dimensional gas chromatography (GC×GC)

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high-resolution mass spectrometry (HRMS) analysis generated a massive amount of data that could be

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used for non-target screening purposes. We present a data reduction and prioritization strategy that

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involves time-trend analysis of non-target data. Sewage sludge collected between 2005 and 2015 in

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Stockholm (Sweden) was retrieved from an environmental specimen bank, extracted and analyzed by

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GC×GC-HRMS. After data alignment features with high blank levels, artifacts and low detection

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frequency were removed. Features that appeared in four to six out of ten years were reprocessed to fill

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in gaps. The total number of compounds was reduced by more than 97% from almost 60,000 to

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almost 1,500. The remaining compounds were analyzed for monotonic (log-linear) and non-

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monotonic (smoother) time trends. In total, 192 compounds with log-linear trends and 120 compounds

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with non-monotonic trends were obtained, respectively. Most compounds described by a log-linear

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trend exhibited decreasing trends and were traffic-related. Compounds with increasing trends included

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UV-filters, alkyl-phenols, and flavor and fragrances, which often could be linked to trade statistics.

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We have shown that non-target screening and stepwise reduction of data provides a simple way of

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revealing significant changes in emissions of chemicals in society.

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Keywords: Gas chromatography, GC×GC, non-target screening, time-trend analysis, sewage sludge,

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data reduction


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Introduction

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Anthropogenic chemicals (for example, pesticides, pharmaceuticals or detergents, and their

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degradation products) are ubiquitous in the environment 1. In 2015, sales of chemicals within the EU

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amounted to 519 billion euro. Consumer chemicals (such as cosmetics, perfumes and soaps) and so-

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called specialty chemicals accounted for 13% and 28%, respectively. Specialty chemicals include

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paints and inks, crop protection, dyes, and pigments 2. Many of these chemicals, including potential

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pollutants, reach urban wastewater streams and, hence, enter sewage treatment plants (STPs). STPs

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are used to remove nutrients as well as some metals and organic chemicals from urban waters.

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Sewage sludge, a solid by-product, is formed during sewage treatment. This sludge contains some of

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the aforementioned nutrients, metals, and organic contaminants. Due to the high content of nutrients,

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sewage sludge is attractive for use as fertilizer in agricultural industry.

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Recent statistics reveal that 54% and 61% of the sewage sludge produced in Europe and North

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America, respectively, are used in land applications. The rest of the sewage sludge is placed in

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landfills, combusted, disposed of, or reused in other ways

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sludge used as fertilizer on agricultural land in Europe or the U.S. must lie within limits stipulated by

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law 5,6. Some EU member states (e.g., Denmark, Finland, Sweden, and the Netherlands) have defined

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additional, stricter, national requirements, which mandate analysis of other contaminants in sludge.

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For example, Sweden stipulates that polychlorinated biphenyl (PCB), nonylphenol ethoxylate (NPE),

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polycyclic aromatic hydrocarbon (PAH), and toluene content must be determined 7. Nonetheless,

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sewage sludge can still be hazardous as many compounds are unregulated and crops can absorb

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pollutants from agricultural soils

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sludge contaminants and on changes in the flux of chemicals from society to the environment.

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Time-trend analysis can be used to reveal the most important of the numerous compounds obtained

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when non-destructive sample clean-up and subsequent full spectrum mass spectrometry (MS) analysis

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(i.e., non-target screening) are performed

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evaluating target analytes

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and, to the best of our knowledge, such analysis has never been performed on sewage sludge. The aim

12–15

8–10

3,4

. The heavy-metal content of sewage

. Hence, there is a need for improved information on sewage

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. Time-trend analysis has been used extensively for

. However, time-trend analysis on non-target MS data are rare


11,16–18

,


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of the present study is to investigate time trends of compounds in sewage sludge and link these trends

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to trade statistics and use of chemicals in society. Traditionally, time-trend analysis has focused on

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log-linear trends

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monotonic trends (e.g., those describing changes in usage patterns of chemicals) can also occur and

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were, for example, observed for PCBs and dichlorodiphenyltrichloroethane (DDT) and, more

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recently, polybrominated diphenyl ethers (PBDEs)

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“smoother function” and subsequently comparing the variance revealed via this function with the

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variance revealed through the log-linear regression

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identified by using a three-point running average as a smoother function.

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Sewage sludge collected over ten years was screened using two complementary extraction and clean-

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up methods and analyzed with comprehensive two-dimensional gas chromatography – high resolution

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mass spectrometry (GC×GC-HRMS)

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time-trend analysis with the aim of identifying sludge contaminants that exhibit statistically

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significant monotonic as well as non-monotonic time trends. Compounds exhibiting increasing trends

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were of special interest.

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Materials and Methods

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Samples

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Sewage sludge samples from 2005 and 2007–2015 were obtained from the Environmental Specimen

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Bank (ESB) at the Swedish Museum of Natural History. A composite sample (n = 5) of dewatered

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anaerobically stabilized (i.e. digested) sewage sludge was collected every fall at the Henriksdal STP

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(Stockholm, Sweden), which serves 750,000 inhabitants. After collection, samples were freeze-dried

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and then stored at -25°C in glass jars in the freezer. For each year three replicate samples were

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prepared, processed and analyzed.

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Sample-treatment methods

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Two previously validated sample preparation methods for GC-MS were used

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freeze-dried sewage sludge samples (1 g) were extracted via pressurized liquid extraction (PLE). The

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, which are characteristic of environmental samples. Nevertheless, non-

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19–22

. These trends are evaluated by applying a

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. In this study, non-monotonic trends were

. The resulting non-target screening data were evaluated via


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. In both methods,

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first method (referred to as PLE hereafter) uses gel permeation chromatography (GPC) as clean-up

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technique. The second method (referred to as selective PLE (SPLE) hereafter) uses an in-cell clean-up

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with silica gel. The two methods are complementary. The first method covers small- and medium-

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sized analytes with a wide range of polarities and the second method covers non-polar analytes of all

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sizes. Prior to extraction, internal standard (IS; 100 ng d10-phenanthrene) was spiked onto the sludge

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and the solvent was evaporated. Details on the PLE procedures are given in

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information (SI). Prior to analysis two volumetric standards were added (13C PCB 97 and 188) for

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volume difference compensation and analyte area normalization.

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Sample analysis

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All samples were analyzed via GC×GC high resolution (> 25,000) time-of-flight (TOF) mass

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spectrometry using a Leco HRT system (St. Joseph, MI, USA). The GC was an Agilent 7890A GC

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equipped with a secondary oven and a quad jet two stage thermal (liquid nitrogen) GC×GC

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modulator. The MS was operated in electron ionization (EI) mode at an energy of 70 eV. A Gerstel

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CIS4 inlet was used in pulsed split-less mode for sample introduction (split-less time: 115 s, inlet

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purge flow: 25 mL/min, septum purge flow rate: 3 mL/min). A 30-m Rxi-5ms and a 1.6-m Rxi-17Sil

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MS column (both with 0.25 mm i.d., 0.25 µm film thickness, and purchased from Restek, Bellefonte,

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PA, USA) were used as first-dimension and second-dimension GC columns, respectively. A

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deactivated capillary was used in the transfer line (0.25 mm i.d.). The following oven temperature

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programs were employed: 60°C for 2 min, 4°C/min to 300°C, and hold for 5 min for the main GC

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oven and 90°C for 2 min, 4°C/min to 300°C, hold for 8 min for the secondary oven. Helium (flow

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rate: 1 mL/min) was used as carrier gas. The modulator had a temperature offset of 15°C relative to

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the secondary oven and a modulation period, hot jet duration, and cold jet duration of 6 s, 0.73 s, and

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2.27 s, respectively, were employed. The transfer line was held at 325°C. The ion source temperature

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was 250°C and 100 spectra per s were recorded for m/z ranging from 38 to 800. In addition to the

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samples, an n-alkane mixture was injected every tenth sample and analyzed to monitor retention time

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shifts and allow calculation of linear retention indices (LRI) according to van den Dool and Kratz 25.


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and in the supporting


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After the data from above were analyzed and compounds were identified as far as possible using the

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workflow described below, molecular-ion information for the identification of additional analytes was

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obtained by using low energy (soft) EI at 15 eV (only a few selected samples were analyzed). The

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analysis was performed on an Agilent 7250 GC QTOF (St Clara, CA, USA) equipped with a Zoex ZX

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2 thermal modulator (Houston, TX, USA). The analytical details are given in the SI. Data were

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aligned using LRI information.

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Data analysis

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Data were acquired and processed using the ChromaTOF software (version 1.90.60, Leco

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Corporation). Peak finding, including peak and spectra deconvolution (i.e. non-target peak picking),

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and library searches were also applied in this software. Afterward, data were exported, transformed

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into unit-resolution using a Python script, and aligned with Guineu 1.0.3 26 (Guineu only handles unit-

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resolution data). To normalize peak areas, the area of the analyte was divided by the area of the

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closest-eluting (first-dimension retention time, 1tR) volumetric standard compound. Features (i) with

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concentrations lower than three times the respective blank value, (ii) that appeared in fewer than two

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out of three replicates, or (iii) appeared in samples from fewer than four out of ten years were

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removed. For statistical reasons a minimum of four data points is required (step (iii)). Choosing an

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even higher threshold, however, might remove interesting compounds that are either new emerging or

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being phased out and, hence, only appear in a limited number of years. Instead, a recursive workflow

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was used to improve the data and statistical analysis by manually filling in gaps for time series

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consisting of only four to six years. At the end, all compounds were detected in samples from at least

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seven out of ten years. Step (ii) assures that no artefacts are included. Furhtermore, compounds that

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are missed during the peak picking process (due to an imperfect algorithm) and, hence, only appear in

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two out of three replicates, are still included.

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The remaining features were subjected to time-trend analyses using the average of three replicates for

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each year. Firstly, log-linear trends were revealed through a linear regression analysis of the

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logarithmic data. Only trends at or below a significance level of α=0.05 were considered. The slope of

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the obtained regression line reflects the yearly increase or decrease in percent. Secondly, non6 ACS Paragon Plus Environment

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monotonic trends were identified using an unweighted 3-point running average smoother function. To

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determine whether the smoother describes the trend better (i.e., by revealing more of the total

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variance) than the log-linear regression line, an analysis of variance (ANOVA) was used (α=0.05) 27.

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Subsequently, trends that were below the significance level for the smoother but not the log-linear

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regression were further scrutinized. Here, trends that had more than one minimum or maximum value

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and, hence, fluctuated more, were excluded since production and consumer preferences generally

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change slowly. This allows, for example, the discovery of compounds whose use peaked and then

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decreased during the 10-year time frame. Extreme values were identified using a 99% prediction

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interval around the smoother function. The underlying data were inspected to ensure proper

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integration and peak assignment for data that included extreme values. Extreme values remaining

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after this inspection were kept in the dataset, and clearly marked as extreme values.

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Since a huge number of consecutive time-trend analyses were carried out, the actual Type-I error rate

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for each test is above the nominal significance level of α=0.05 28. However, in the initial phase, the

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time trend analyses were used to select potentially interesting compounds from the total amount of

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compounds and we can expect several of the selected compounds to show a trend just by chance. In

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the text, increasing and decreasing trends or non-monotonic trends, thus, refers to trends that showed

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p-values below the the nominal significance level of α=0.05. Those trends are not necesarily

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significant at a 5% level. In the final stage, time-series of the determined compound are compared

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with figures on production and/or consumption that can support the trends indicated by statistical

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analyses.

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Finally, an estimation of concentrations was performed for all compounds with increasing log-linear

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trends (and for five additional compounds shown in figures). The total areas of all fragments of a

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compound of interest were divided by the total area of all fragments of the internal standard (d10-

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phenanthrene) and then multiplied with the amount of internal standard (100 ng) and divided by the

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sample weight. This semi-quantification was performed using the maximum peak area of each

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compound.

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Identification of unknown compounds 7 ACS Paragon Plus Environment


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Compounds that exhibit a log-linear or non-monotonic trend were identified using a library search

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(NIST MS library (2011)) in the first step. The suggested hits were manually evaluated by their

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spectral match, mass accuracy and LRI matching. Major sample spectrum features had to be

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explained and all major features in the libray specrum had to be present in the sample spectrum.

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Abundant ions had to be within 5 ppm mass error. In cases when an experimental LRI was available it

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had to be within 40 RI units. Predicted LRIs (NIST group contribution model 29) had to be within the

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uncertainty range given.

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For compounds absent in NIST, molecular and fragment ion information obtained through normal 70

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eV or low energy EI were used in subsequent steps: molecular formula generation and application of

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the in-silico fragmentation tool MetFrag 30 using the ChemSpider database and allowing a 5ppm error.

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Compounds that had higher data source or reference counts in ChemSpider were hereby preferred

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(data source and reference count were weighed 10% each, and spectral similarity was weighed 100%).

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In addition, possible molecular formulae were calculated for unidentified compounds using the exact

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mass of the suspected molecular ion. An example of the identification of compounds not present in

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NIST is given in the results section. Compounds that remained unknown after these steps were

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labeled as such and included in the SI (Table S1).

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Results and discussion

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Reducing the amount of data

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The amount of data was stepwise reduced, as shown in Figure 1. The initial step of the pre-selection

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includes all peaks that were detected and automatically aligned in the Guineu software. At this stage,

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tens of thousands of peaks (more than 22,000 peaks for PLE and more than 36,000 peaks for SPLE)

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are recognized. Only a few of these occur in the blanks above the set threshold. However, many

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components occur in only one of three replicates (mainly septum, column and injector liner bleed)

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and, therefore, the replicate filter reduced the data size by an order of magnitude. Similarly, allowing

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only peaks that appear in data from at least four out of ten years also reduced the data size. In the last

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step, 75% and 70% of the peaks are removed from the PLE and SPLE data sets, respectively. Overall,


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the filtering yields a 97% and 98% size reduction of the PLE and SPLE datasets, respectively. The

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steps shown in Figure 1 start with data processing and end with time trend analysis. This part is quick

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and can be done in a day or two depending on how advanced the routines are. The most time-

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consuming step, however, follows then: the identification of compounds. The amount of time it takes

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to identify compounds depends on the number of compounds that need to be identified by manual

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interpretation or using in-silico tools. In general, this step takes weeks rather than days and automated

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ways for, for example, in-silico identification would speed up the process considerably.

DP: Peak picking

Pre-treatment • Alignment • Normalisation

PLE: 22,395 peaks SPLE: 36,560 peaks

Filling gaps for compounds with n=4 to 6

Trend analysis

Pre-selection

Blank removal

Replicates • 2 out of 3

• Limit: 3× the blank



Trend analysis

Minimum number of detected years • Limit: 4 out of 10

PLE: 685 peaks SPLE: 813 peaks

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Figure 1 Workflow scheme for the data treatment. DP: data processing.

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Monotonic trends

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The analysis of the data revealed 192 compounds with log-linear trends below the significance level

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(106 for PLE and 107 for SPLE), where 60 and 132 exhibit increasing and decreasing trends,

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respectively. As the numbers show, the PLE and SPLE datasets overlap. In total, 21 compounds (i.e.,

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11 polycyclic aromatic hydrocarbons (PAHs) or PAH derivatives, five alkylbenzenes, three other

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hydrocarbons, trans-α-bergamotene, and 7-pentyl-bicyclo[4.1.0]heptane (a disinfection by-product))

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exhibit a log-linear trend and are detected through both methods. In all cases the trends indicated by

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the two methods concur. The results of the log-linear regression of all compounds with significant

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monotonic trends are shown in Table S2.

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The compounds with significant trends are divided into seven classes, five of which are based on

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structural characteristics (PAH and PAH derivatives, alkylbenzenes, other hydrocarbons, steroidal



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substances, and aldehydes and ketones) and two diverse classes which are further specified in Table 1

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(flavor and fragrance compounds and “other compounds”). The last of these classes (i.e., “other

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compounds”) includes all compounds that are incompatible with the other categories, including

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unidentified compounds. This group, i.e., “other compounds”, is also the largest group among

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compounds that exhibit increasing log-linear trends (Figure 2). In addition, this group accounts for a

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large fraction (17%) of decreasing log-linear trends (see Table 1 for the compounds comprising this

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group).

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Figure 2 Number of significantly increasing (left) and decreasing (right) log-linear trends. The numbers before the slash and after the slash (in the slices) represent the compounds detected via the PLE and SPLE method, respectively. The group of “other compounds” also includes unidentified compounds.

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The largest group of the remaining compounds with increasing log-linear trends consist of flavor- and

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fragrance-related compounds (see Table 1 for details). Use of natural substances in personal care

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products and imported values of essential oils, perfume, and flavor material to Sweden increased

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during the study period

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fragrance-related compounds. The remaining groups of compounds account for a rather small fraction

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(20%) of compounds with increasing trends.

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PAHs, hydrocarbons, and alkylbenzenes, account, collectively, for 70% of the decreasing trends

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(right-hand side Figure 2). Emissions of these compounds can be correlated with traffic. Although the

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number of cars using gasoline or diesel engines in the Stockholm area have increased 33, technological

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advancements may have resulted in the decreasing trends observed. Fuel consumption of vehicles per

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kilometer has decreased and combustion efficiency as well as catalyst performance have improved

31,32

, thereby resulting in predominantly increasing trends for flavor- and


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over time, resulting possibly in decreased PAH and alkylbenzene emissions. Interestingly, the only

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four PAH derivatives that exhibit an increasing trend are partly or completely saturated.

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In the discussion on monotonic trends for individual compounds we will first discuss the consumer

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chemicals and then the so-called specialty chemicals. Among the identified compounds in the group

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of consumer chemicals (Table 1) are several pharmaceuticals and personal care products (PPCPs).

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The strongest increasing trend (+41% per year) is observed for Homosalate, a UV filter (Figure 3).

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Another UV filter, Octocrylene, also exhibits a strong increasing trend (+19% per year, Figure 3).

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Both compounds are used in sunscreens and have both been previously (i.e., in 2009 and 2014)

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detected in sewage sludge from the STP in Stockholm, with concentrations being higher in 2014 than

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in 2009

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increasing trends. The semi-quantification calculations indicate a higher concentration for

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Octocrylene (11 µg/g) than for Homosalate (1.1 µg/g). In Sweden maximum limits for concentrations

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of a few pollutants in sewage sludge are given for sludge that is intended for soil amendment. Those

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limits vary for different compound groups and are highest for nonylphenols and NPEs (100 µg/g). A

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comparison shows that Octocrylene and Homosalate concentrations exceed the limit for PCBs (0.4

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µg/g) while Octocrylene also exceeds the limit for PAHs (3 µg/g) and toluene (5 µg/g). The PPCPs

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category also consists of disinfectants. Among the disinfectants, Triclosan has exhibited a decreasing

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trend in sewage sludge collected from several different STPs across Sweden

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work, Triclosan is characterized by a steep decrease of 18% per year (Figure 3). A significant

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decreasing log-linear trend (-9% per year) is also observed for 7-pentyl-bicyclo[4.1.0]heptane (Figure

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3), which is probably a disinfection by-product. This work represents the first-ever report of

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7-pentyl-bicyclo[4.1.0]heptane in environmental samples

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Finally, a medium strong increase (+8% per year) is observed for 4-(3,4-dichlorophenyl) tetralone

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(Figure 3), an impurity in, as well as a metabolite of, the pharmaceutical Sertraline

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increasing trend of 4-(3,4-dichlorophenyl) tetralone may be associated with increased use of Sertraline

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in the Stockholm area between 2006 and 2015

34,35

. In addition, usage of these chemicals has increased

40

36,37

and, hence, explains the

14

. Similarly, in this

38,39

. The

(Figure S1). The semi-quantification calculations



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indicate that 4-(3,4-dichlorophenyl) tetralone is present in the sludge in concentrations up to 0.53 µg/g

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dry sludge, i.e. similar to the threshold of PCBs.

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Another group of compounds that is linked to the group of PPCPs are flavor and fragrance

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compounds. Many of these compounds, as for example musk related compounds, including

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Galaxolide, are used in perfumes and other personal care products. Those synthetic musks, including

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isomers and impurities 41, 42, show similar increasing trends here in sewage sludge, ranging from +4%

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to +6% (Table 1). Similarly, use of Galaxolide in Sweden has been increasing from 2005 to 2013 43.

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Furthermore, a general increase in concentrations of synthetic musks in the environment over time,

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particularly in the marine environment, has been observed 44,45.

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The largest increase among flavor and fragrances was obtained for Verdyl acetate, an odor agent used

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in cleaning products but also personal care products (Table 1). The increase cannot be explained by

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an increased use. The registered use of Verdyl acetate in Sweden between 2005 and 2014 was almost

270

constant 43.

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272 273 274 275 276 277

Figure 3 Time trend of Homosalate, Octocrylene, Triclosan, 7-Pentyl-bicyclo[4.1.0]heptane, 4-(3,4dichlorophenyl) tetralone and 2,5-dichloroaniline. In the figures the number of data points (n(tot)), the yearly change in percent (slope) including its 95%-confidence interval, the coefficient of determination (r²) and the pvalue are shown. The number on the vertical axis indicates the concentration of the highest point from the semiquantification approach.

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Other flavor and fragrance compounds that were found with significant increasing monotonic trends

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are trans-β-ionone, farnesyl acetone, humulene (also called α-caryophyllene), β-caryophyllene and

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caryophyllene oxide, with medium strong increasing trends ranging from 5% to 9% per year. Only for

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β-caryophyllene and trans-β-ionone use in Sweden is reported. The number of preparations using

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caryophyllene and trans-β-ionone has been increasing during the studied period

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in the increasing time trend.

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Among the identified consumer chemicals were also three food constituents, which were not in the

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NIST library and had to be identified using the non-target identification workflow. Molecular formula

286

generation using the apparent molecular ion resulted in tentative formulas C17H28O, C19H32O, and

287

C19H34O. Further evaluation of fragmentation patterns using MetFrag with ChemSpider as structure

288

database indicated that the compounds might be alkyl/alkenyl-substituted furans, specifically

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avocadenofuran, 2-(1-pentadecenyl)furan and 2-pentadecylfuran. Those belong to the so-called

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avocadofurans, which are, as the name implies, natural constituents of avocado. EI spectra were

291

obtained using literature searches and were found to match those of the sludge contaminants (Table

292

S3). The three avocadofurans had similar increasing trends ranging from 18% to 25% per year;


46

which is reflected


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consistent with a common source, possibly avocado. The avocado import to Sweden increased by

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17% per year over the period 2006 to 2013 47,i.e. mirroring the sludge trends.

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Many of the specialty chemicals (Table 1) are used as plastic additives. Diethyl phthalate and

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tri(2-butoxyethyl) phosphate (TBEP; Figure S2) exhibit decreasing time trends of -4% and -13% per

297

year, respectively, concurring with the general decline of use between 2005 and 2015 46. In addition,

298

decreasing trends of -13% and -11% are observed for two short chain alkyl biphenyls, one methylated

299

and the other ethylated or di-methylated, respectively. Alkylated biphenyls are commonly used as

300

plasticizers in industrial applications

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and 4-octyl-N-(4-octylphenyl)-benzenamine, exhibit an increasing and a decreasing monotonic trend,

302

respectively, of +8% and -6% per year. Documented use of 4-hexadecylbiphenyl as a plasticizer is

303

lacking, but (as previously mentioned) alkylated biphenyls are commonly used as plasticizers

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The use of 4-octyl-N-(4-octylphenyl)-benzenamine in plastic and rubber products and occurrence in

305

environmental samples have previously been reported 51. The decreasing trend observed in this work

306

is consistent with decreased 4-octyl-N-(4-octylphenyl)-benzenamine usage between 2005 and 2015 43.

307 308 309 310

Table 1 Compounds with trends with p-values below the nominal significance level (α=0.05) divided in categories by their use: Yearly change for log-linear trends, significant non-monotonic trends (smoother), and maximum concentrations from a semi-quantification approach are given (additional information can be found in Table S1). Compound

LogLinear

48–50

Smoother

SPECIALTY CHEMICALS Plastic additives Tri(2-butoxyethyl) phosphate (TBEP) Methylbiphenyl Ethylbiphenyl 4-Octyl-N-(4-octylphenyl) benzenamine Diethylphthalate 4-Hexadecylbiphenyl Unknown Phthalate 1 Unknown Phthalate 2 Surface active compounds Nonylphenol isomer 2 Nonylphenol isomer 4 Nonylphenol isomer 1 Nonylphenol isomer 3 4-tert-Octylphenola 1-Methyl-3-nonylindane or 1- Methyl-4-octyl-1,2,3,4tetrahydronaphthalene

. The last plastic additives considered, 4-hexadecylbiphenyl

Semiquant. Compound (µg/g) CONSUMER CHEMICALS

-13%

-13% -11% -6%

-4% 8% X X

-18% -10% -10% -8%

0.54 0.035 5.5

X

X

0.73

X

Miscellaneous

Flavor and fragrances Compound c* Similar to Tonalid Galaxolide isomer Galaxolide isomer β-Caryophyllene Galaxolide isomer Humulene Farnesyl acetone Compound d Compound e b Tridecanal Caryophyllene oxide 2,4-Decadienal trans-α-Bergamotene b trans-β-Ionone Curcumene b α-Isomethyl ionone Heptyl salicylate Verdyl acetate Calamenene b 6-Methyl-5-hepten-2-one 3-Octanone


Log- SmooLinear ther

-13% 4% 4% 4% 5% 6% 6% 6% 6% 6% 7% 7% 7% 8% 9% 10% 16% 24% 145%

X

X

X X X

48–50

.

Semiquant. (µg/g)

1.2 0.51 0.54 1.1 0.23 0.30 0.72 0.84 0.092 1.8 1.0 0.072 0.10 0.45 0.85 0.35 0.39 1.7

Page 15 of 26


1-(3-Fluoropropyl)-4-(hexyloxy)benzene Compound a* Compound b* Pentadecyl phenyl ester carbonate Pentadecyl phenyl ester carbonate Pentadecyl phenyl ester carbonate Pentadecyl phenyl ester carbonate Tetradecyl phenyl ester carbonate 6,9-Pentadecadien-1-ol Tetradecyl phenyl ester carbonate Pentadecadienyl-phenol 2,5-Dichloroaniline

311 312 313 314 315 316 317

-10% -3% 2% 6% 10% 10% 10% 10% 10% 14% 19% 30%

X X X

0.29 0.12 0.14 0.14 0.19 0.18 0.44 0.22 10 0.37

3-Carene 2-Nonanone 4-tert-Butylcyclohexyl acetate 2-(Phenylmethylene)octanal 3-Nonanone trans-Anethole Lilial Food constituents 2-(1-Pentadecenyl)furan (C19H32O) Avocadenofuran (C17H28O) 2-Pentadecylfuran (C19H34O)

X X X X X X X 19% 20% 29%

PPCPs Triclosan -18% 7-Pentyl-bicyclo[4.1.0]heptane -9% 4-(3,4-Dichlorophenyl) tetralone 8% Octocrylene 19% Homosalate 41% *Compound a: 2-(2,3-Dihydro-1H-inden-1-yl)-1-(4-{[(E)-2-phenylvinyl]sulfonyl}-1-piperazinyl)ethanone *Compound b: 14,14-Bis(2-methylenecyclopropyl)-13,15-dioxa-14-siladispiro[5.0.5.3]pentadecane *Compound c: 1-(1,3,4,4a,5,6,7-Hexahydro-2,5,5-trimethyl-2H-2,4a-ethanonaphthalen-8-yl)ethanone *Compound d: (7a-Isopropenyl-4,5-dimethyloctahydroinden-4-yl)methanol *Compound e: 2-Isopropyl-5-methyl-9-methylene-bicyclo[4.4.0]dec-1-ene a: extreme value in 2013 b: Fragrance properties not described, but documented constituent of essential oil or plant extractives


4.4 0.39 5.1

X

0.30 0.11 0.53 11 1.1


318

The specialty chemicals also include several compounds related to surfactants. Four of these

319

compounds are nonylphenol isomers. These are most likely used for the same purpose and, hence, the

320

decreasing trend observed in each case is unsurprising. The compound 4-tert-octylphenol is also part

321

of this group. However, unlike the aforementioned four compounds, this compound exhibits a

322

significant non-monotonic trend and, hence, will be discussed in the next section. Both, octylphenols

323

and nonylphenols, are used as dispersing or emulsifying agents 52 and in the production of surfactants

324

(nonylphenol and octylphenol etoxylates). Similar to the nonylphenol trends found in this work, the

325

use of NPEs and nonylphenols in Sweden decreased between 2005 and 2015 46.

326

Among the remaining specialty chemicals only one, 2,5-dichloroaniline, which is used as a pesticide

327

43

328

per year (Figure 3)). This compound was previously reported in sewage sludge samples 54, but import

329

statistics for Sweden are unavailable. Alkyl phenyl ester carbonates exhibit a medium strong increase

330

(ca. +10% per year). These compounds can be extracted from coal and may be used in industrial

331

production processes

332

reported. Other compounds in this category are mainly used in small scale (academic) organic

333

synthesis studies, and the reason for their occurrence in the sludge remains unclear.

334

Finally, compounds that belong to the group of unknown chemicals remain to be identified. Emphasis

335

is placed on compounds characterized by strong increasing trends, i.e., Unknown 7, Unknown 8, and

336

Unknown 11. A first evaluation indicates that Unknown 7 has the molecular formula C16H33N3O4S

337

and Unknown 8 has the molecular formula C24H31NO4. More information about these compounds and

338

their spectra can be found in the SI (Table S1, S4 and Figure S3-S18). Within this group, the steepest

339

trend is observed for Unknown 11 (+150% per year). This dramatic increase is rather uncommon but

340

can happen shortly after a new chemical has been released on the market. Trends that are significant

341

by chance may also show an extreme slope. This effect is related to the so-called magnitude error (m-

342

error)

343

degrades during storage.

and in the production of dyes and pigments 53, exhibits a strong increasing trend (in this case, +30%

56,57

55

. Other occurrences of these compounds have, to our knowledge, not been

. Furthermore, the compound may be volatile and, hence, evaporate, or is unstable and


Page 16 of 26

Page 17 of 26


344

In general, degradation and evaporation should play a minor role since samples are freeze-dried

345

before storage and stored in sealed containers, in the dark, at low temperatures. However, some

346

evaporation can still occur for semi-volatile sample constituents

347

may still be active at low temperatures and can biodegrade sample constituents. Some biogenic

348

compounds are readily biodegradable, e.g. fatty acids (through beta-oxidation) and small petroleum

349

constituents such as aliphatics, naphthalene and alkylated naphthalenes 59. Anthropogenic compounds

350

with similar structures may also be vulnerable to enzymatic degradation. In case of Unknown 11, the

351

EI spectrum (Figure S13) indicates a low molecular weight (M+ at m/z 136) and, therefore, losses via

352

evaporation must be considered.

353

Non-monotonic trends

354

In addition to log-linear trends, non-monotonic trends are evaluated using a smoother function 27. In

355

total, 120 different compounds with significant non-monotonic trends are detected, 43 in the PLE

356

dataset and 95 in the SPLE dataset. If a compound exhibits a significant log-linear trend and a

357

significant non-monotonic trend, the smoother describes the trend better than the log-linear curve. In

358

total, 46 compounds (incl., 14 hydrocarbons, eight PAHs, eight alkylbenzenes, six natural substances,

359

tridecanal, two alkyl phenyl carbonates, one nonylphenol, and 7-pentyl-bicyclo[4.1.0]heptane) exhibit

360

both types of trends.

361

The largest group of compounds characterized by non-monotonic time trends are hydrocarbons, which

362

account for almost 50% of the compounds (Figure 4). Many of these hydrocarbons exhibit a similar

363

trend: a decrease observed in the initial period followed by an increase for the later part of the time

364

series. A large share (21%) is covered by PAHs and alkylbenzenes, which are, in principle, also

365

hydrocarbons. More than half of these PAHs and alkylbenzenes exhibit a decreasing log-linear trend

366

at the same time while the remaining compounds seems to be mostly decreasing as well, but do not

367

exhibit a significant log-linear trend. The group of other compounds (Table 1) covers a similar share

368

as the alkylbenzenes (10%). The remaining groups (aldehydes and ketones, flavor and fragrances and

369

steroids) account for relatively small shares.


58

, and, moreover, some enzymes


370

Flavor and fragrance related compounds are one of the groups with fewer non-monotonic trends. This

371

group has a share of 10% among non-monotonic trends. Tridecanal not only exhibits a significant

372

non-monotonic trend but also a significant log-linear trend. All remaining flavor and fragrances have

373

a similar trend: the concentration increases till the middle of the studied period and then decreases

374

again. This indicates a similar use pattern for those compounds. However, only for Lilial (Figure

375

S19) and 3-carene, two perfuming agents, use statistics for Sweden are available that can confirm this

376

trend and have a similar peak in the middle of the studied period 43.

377 378 379 380

Figure 4 Number of significant non-monotonic trends (smoother). The numbers before the slash and after the slash in the slices represent the compounds detected via PLE and SPLE, respectively. The group of “other compounds” also includes all unidentified compounds.

381

One of the constituents of the “other compounds” group is 4-tert-octylphenol. Octylphenol is used (i)

382

as an intermediate in the production of phenolic resins, which are used in the production of rubber and

383

inks and (ii) in the production of surfactants (octylphenol etoxylates). The concentration of this

384

compound in the sludge increases up to 2013 and decreases thereafter (Figure 5). In 2011,

385

octylphenol was added to the REACH convention and classified as a “substance of very high

386

concern”. In reaction to this classification, manufacturers may have started searching for replacements

387

in case that octylphenol is banned soon. This could explain the post-2013 decreasing trend of this

388

compound. The maximum concentration observed in the current study, 0.73 µg/g, is much below the

389

threshold for nonylphenols and NPEs (100 µg/g) in soil amendment in Sweden.

390

In the group of plastic additives, two phthalates are characterized by a non-monotonic trend (Figure

391

5). Both phthalates elute in the range of dinonyl phthalates during the GC run (1tR). The separation on 18 ACS Paragon Plus Environment

Page 18 of 26

Page 19 of 26


392

the non-polar first dimension stationary phase is performed on the basis of boiling points and, hence,

393

the unknown phthalates are most likely diisononyl phthalate (DINP) isomers. Similar to the time trend

394

for the two phthalates shown here in Figure 5, use of DINP in Sweden decreased after 2010

395

likely because of increased use of non-phthalate plasticizers. In addition, one of the two phthalates

396

was present at a rather high concentration (5.5 µg/g) around year 2010, but its concentration has

397

fortunately dropped by a factor of 3 until 2015.

43,46

,

398 399 400 401 402

Figure 5 Time trend of octylphenol and two different phthalates. In the figures the number of data points (n), and the results from the ANOVA comparing the smoother with a regression analysis are shown. The number on the vertical axis indicates the concentration of the highest point from the semi-quantification approach. The extreme value for 4-tert-octylphenol in 2013 is indicated by a bold black line around the circle.

403

Finally,

404

impurities of linear alkyl benzene commercial mixtures

405

benzene sulfonates; LAS) is present in the sewage sludge, characterized by a non-monotonic trend

406

(Figure

407

tetrahydronaphthalene, have never been previously reported in environmental samples.

408

Outlook

409

The measures taken for reducing the amount of data and revealing compounds of potential concern

410

have proven effective. The total number of peaks detected via the PLE and SPLE method was reduced

411

by 97% and 98%, respectively. In addition, the time-trend analysis revealed many compounds, which

412

were characterized by increasing log-linear trends. Moreover, many significant non-monotonic trends

1-methyl-3-nonylindane

S20).

Both

or

compounds,

1-methyl-4-octyl-1,2,3,4-tetrahydronaphthalene 60

(technical

which are used to produce linear alkyl

1-methyl-3-nonylindane


and

1-methyl-4-octyl-1,2,3,4-


413

were detected. However, non-monotonic trends are (in general) of greater importance when longer

414

time spans, than those considered in this work, are analyzed. To complete the comprehensive

415

screening and time-trend analysis, an LC-HRMS-based method has also been developed. This method

416

will be applied to the same samples including a similar data-reduction strategy as presented here. The

417

GC×GC-HRMS and LC-HRMS datasets are expected to be complementary and, hence, cover a large

418

part of the chemical-property space. Both sets of data will be stored in a digital archive and used in

419

retrospective analysis. Altogether, this will provide new powerful tools for time-trend studies, or

420

spatial-trend studies, which can reveal significant changes in use and emissions of well-known,

421

emerging and new chemicals in society.

422

Acknowledgements

423

We thank the Swedish Museum of Natural History and especially Ylva Lind for providing the

424

samples from the Environmental Specimen Bank. We would like to thank the Swedish EPA for

425

funding through their Environmental Screening Program (Contract 2219-13-002).

426

Supporting Information Available

427

Part of the methods including materials, trend data for all log-linear trends, specific information for

428

unknown compounds (spectra and spectra interpretation), trends for additional compounds in

429

comparison with their use are included. This information is available free of charge via the Internet at

430

http://pubs.acs.org.

431 432

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Non-target screening and time-trend analysis of sewage sludge

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