Nontarget Screening and Time-Trend Analysis of Sewage Sludge

Article Cite This: Environ. Sci. Technol. 2018, 52, 7813−7822

pubs.acs.org/est

Nontarget 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† †

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

‡

Environ. Sci. Technol. 2018.52:7813-7822. Downloaded from pubs.acs.org by UNIV OF NEW ENGLAND on 09/29/18. For personal use only.

S Supporting Information *

ABSTRACT: Nondestructive sample cleanup and comprehensive two-dimensional gas chromatography (GC×GC) high-resolution mass spectrometry (HRMS) analysis generated a massive amount of data that could be used for nontarget screening purposes. We present a data reduction and prioritization strategy that involves time-trend analysis of nontarget data. Sewage sludge collected between 2005 and 2015 in Stockholm (Sweden) was retrieved from an environmental specimen bank, extracted, and analyzed by GC×GC-HRMS. After data alignment features with high blank levels, artifacts and low detection frequency were removed. Features that appeared in four to six out of ten years were reprocessed to fill in gaps. The total number of compounds was reduced by more than 97% from almost 60 000 to almost 1500. The remaining compounds were analyzed for monotonic (log−linear) and nonmonotonic (smoother) time trends. In total, 192 compounds with log−linear trends and 120 compounds with nonmonotonic trends were obtained, respectively. Most compounds described by a log−linear trend exhibited decreasing trends and were traffic-related. Compounds with increasing trends included UV-filters, alkyl-phenols, and flavor and fragrances, which often could be linked to trade statistics. We have shown that nontarget screening and stepwise reduction of data provides a simple way of revealing significant changes in emissions of chemicals in society.

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stipulated by law.5,6 Some EU member states (e.g., Denmark, Finland, Sweden, and The Netherlands) have defined additional, stricter, national requirements, which mandate analysis of other contaminants in sludge. For example, Sweden stipulates that polychlorinated biphenyl (PCB), nonylphenol ethoxylate (NPE), polycyclic aromatic hydrocarbon (PAH), and toluene content must be determined.7 Nonetheless, sewage sludge can still be hazardous as many compounds are unregulated and crops can absorb pollutants from agricultural soils.8−10 Hence, there is a need for improved information on sewage sludge contaminants and on changes in the flux of chemicals from society to the environment. Time-trend analysis can be used to reveal the most important of the numerous compounds obtained when nondestructive sample cleanup and subsequent full spectrum mass spectrometry (MS) analysis (i.e., nontarget screening) are performed.11 Timetrend analysis has been used extensively for evaluating target analytes.12−15 However, time-trend analysis on nontarget MS

INTRODUCTION Anthropogenic chemicals (for example, pesticides, pharmaceuticals, or detergents, and their degradation products) are ubiquitous in the environment.1 In 2015, sales of chemicals within the EU amounted to 519 billion euro. Consumer chemicals (such as cosmetics, perfumes, and soaps) and socalled specialty chemicals accounted for 13% and 28%, respectively. Specialty chemicals include paints and inks, crop protection, dyes, and pigments.2 Many of these chemicals, including potential pollutants, reach urban wastewater streams and, hence, enter sewage treatment plants (STPs). STPs are used to remove nutrients as well as some metals and organic chemicals from urban waters. Sewage sludge, a solid byproduct, is formed during sewage treatment. This sludge contains some of the aforementioned nutrients, metals, and organic contaminants. Due to the high content of nutrients, sewage sludge is attractive for use as fertilizer in agricultural industry. Recent statistics reveal that 54% and 61% of the sewage sludge produced in Europe and North America, respectively, are used in land applications. The rest of the sewage sludge is placed in landfills, combusted, disposed of, or reused in other ways.3,4 The heavy-metal content of sewage sludge used as fertilizer on agricultural land in Europe or the U.S. must lie within limits © 2018 American Chemical Society

Received: Revised: Accepted: Published: 7813

February 28, 2018 June 2, 2018 June 13, 2018 June 14, 2018 DOI: 10.1021/acs.est.8b01126 Environ. Sci. Technol. 2018, 52, 7813−7822

Article

Environmental Science & Technology

(both with 0.25 mm i.d., 0.25 μm film thickness, and purchased from Restek, Bellefonte, PA, U.S.A.) were used as firstdimension and second-dimension GC columns, respectively. A deactivated capillary was used in the transfer line (0.25 mm i.d.). The following oven temperature programs were employed: 60 °C for 2 min, 4 °C/min to 300 °C, and hold for 5 min for the main GC oven and 90 °C for 2 min, 4 °C/min to 300 °C, hold for 8 min for the secondary oven. Helium (flow rate: 1 mL/min) was used as carrier gas. The modulator had a temperature offset of 15 °C relative to the secondary oven and a modulation period, hot jet duration, and cold jet duration of 6, 0.73, and 2.27 s, respectively, were employed. The transfer line was held at 325 °C. The ion source temperature was 250 °C and 100 spectra per s were recorded for m/z ranging from 38 to 800. In addition to the samples, an n-alkane mixture was injected every tenth sample and analyzed to monitor retention time shifts and allow calculation of linear retention indices (LRI) according to van den Dool and Kratz.25 After the data from above were analyzed and compounds were identified as far as possible using the workflow described below, molecular-ion information for the identification of additional analytes was obtained by using low energy (soft) EI at 15 eV (only a few selected samples were analyzed). The analysis was performed on an Agilent 7250 GC QTOF (St Clara, CA, U.S.A.) equipped with a Zoex ZX 2 thermal modulator (Houston, TX, U.S.A.). The analytical details are given in the SI. Data were aligned using LRI information. Data Analysis. Data were acquired and processed using the ChromaTOF software (version 1.90.60, Leco Corporation). Peak finding, including peak and spectra deconvolution (i.e., nontarget peak picking), and library searches were also applied in this software. Afterward, data were exported, transformed into unit-resolution using a Python script, and aligned with Guineu 1.0.326 (Guineu only handles unit-resolution data). To normalize peak areas, the area of the analyte was divided by the area of the closest-eluting (first-dimension retention time, 1tR) volumetric standard compound. Features (i) with concentrations lower than three times the respective blank value, (ii) that appeared in fewer than two out of three replicates, or (iii) appeared in samples from fewer than four out of ten years were removed. For statistical reasons a minimum of four data points is required (step (iii)). Choosing an even higher threshold, however, might remove interesting compounds that are either new emerging or being phased out and, hence, only appear in a limited number of years. Instead, a recursive workflow was used to improve the data and statistical analysis by manually filling in gaps for time series consisting of only four to six years. At the end, all compounds were detected in samples from at least seven out of ten years. Step (ii) assures that no artifacts are included. Furthermore, compounds that are missed during the peak picking process (due to an imperfect algorithm) and, hence, only appear in two out of three replicates, are still included. The remaining features were subjected to time-trend analyses using the average of three replicates for each year. First, log− linear trends were revealed through a linear regression analysis of the logarithmic data. Only trends at or below a significance level of α = 0.05 were considered. The slope of the obtained regression line reflects the yearly increase or decrease in percent. Second, nonmonotonic trends were identified using an unweighted 3-point running average smoother function. To determine whether the smoother describes the trend better (i.e., by revealing more of the total variance) than the log−linear regression line, an analysis of variance (ANOVA) was used (α =

data are rare,11,16−18 and, to the best of our knowledge, such analysis has never been performed on sewage sludge. The aim of the present study is to investigate time trends of compounds in sewage sludge and link these trends to trade statistics and use of chemicals in society. Traditionally, time-trend analysis has focused on log−linear trends,12,13 which are characteristic of environmental samples. Nevertheless, nonmonotonic trends (e.g., those describing changes in usage patterns of chemicals) can also occur and were, for example, observed for PCBs and dichlorodiphenyltrichloroethane (DDT) and, more recently, polybrominated diphenyl ethers (PBDEs).19−22 These trends are evaluated by applying a “smoother function” and subsequently comparing the variance revealed via this function with the variance revealed through the log−linear regression.23 In this study, nonmonotonic trends were identified using a three-point running average as a smoother function. Sewage sludge collected over ten years was screened using two complementary extraction and cleanup methods and analyzed with comprehensive two-dimensional gas chromatography− high resolution mass spectrometry (GC×GC-HRMS).24 The resulting nontarget screening data were evaluated via time-trend analysis with the aim of identifying sludge contaminants that exhibit statistically significant monotonic as well as nonmonotonic time trends. Compounds exhibiting increasing trends were of special interest.

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MATERIALS AND METHODS Samples. Sewage sludge samples from 2005 and 2007−2015 were obtained from the Environmental Specimen Bank (ESB) at the Swedish Museum of Natural History. A composite sample (n = 5) of dewatered anaerobically stabilized (i.e., digested) sewage sludge was collected every fall at the Henriksdal STP (Stockholm, Sweden), which serves 750 000 inhabitants. After collection, samples were freeze-dried and then stored at −25 °C in glass jars in the freezer. For each year three replicate samples were prepared, processed, and analyzed. Sample-Treatment Methods. Two previously validated sample preparation methods for GC-MS were used.24 In both methods, freeze-dried sewage sludge samples (1 g) were extracted via pressurized liquid extraction (PLE). The first method (referred to as PLE hereafter) uses gel permeation chromatography (GPC) as cleanup technique. The second method (referred to as selective PLE (SPLE) hereafter) uses an in-cell cleanup with silica gel. The two methods are complementary. The first method covers small- and mediumsized analytes with a wide range of polarities and the second method covers nonpolar analytes of all sizes. Prior to extraction, internal standard (IS; 100 ng d10-phenanthrene) was spiked onto the sludge and the solvent was evaporated. Details on the PLE procedures are given in24 and in the Supporting Information (SI). Prior to analysis two volumetric standards were added (13C PCB 97 and 188) for volume difference compensation and analyte area normalization. Sample Analysis. All samples were analyzed via GC×GC high resolution (>25 000) time-of-flight (TOF) mass spectrometry using a Leco HRT system (St. Joseph, MI, U.S.A.). The GC was an Agilent 7890A GC equipped with a secondary oven and a quad jet two stage thermal (liquid nitrogen) GC×GC modulator. The MS was operated in electron ionization (EI) mode at an energy of 70 eV. A Gerstel CIS4 inlet was used in pulsed split-less mode for sample introduction (split-less time: 115 s, inlet purge flow: 25 mL/min, septum purge flow rate: 3 mL/min). A 30-m Rxi-5 ms and a 1.6-m Rxi-17Sil MS column 7814

DOI: 10.1021/acs.est.8b01126 Environ. Sci. Technol. 2018, 52, 7813−7822

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

0.05).27 Subsequently, trends that were below the significance level for the smoother but not the log−linear regression were further scrutinized. Here, trends that had more than one minimum or maximum value and, hence, fluctuated more, were excluded since production and consumer preferences generally change slowly. This allows, for example, the discovery of compounds whose use peaked and then decreased during the 10-year time frame. Extreme values were identified using a 99% prediction interval around the smoother function. The underlying data were inspected to ensure proper integration and peak assignment for data that included extreme values. Extreme values remaining after this inspection were kept in the data set, and clearly marked as extreme values. Since a huge number of consecutive time-trend analyses were carried out, the actual Type-I error rate for each test is above the nominal significance level of α = 0.05.28 However, in the initial phase, the time trend analyses were used to select potentially interesting compounds from the total amount of compounds and we can expect several of the selected compounds to show a trend just by chance. In the text, increasing and decreasing trends or nonmonotonic trends, thus, refer to trends that showed p-values below the nominal significance level of α = 0.05. Those trends are not necessarily significant at a 5% level. In the final stage, time-series of the determined compound are compared with figures on production and/or consumption that can support the trends indicated by statistical analyses. Finally, an estimation of concentrations was performed for all compounds with increasing log−linear trends (and for five additional compounds shown in the figures). The total areas of all fragments of a compound of interest were divided by the total area of all fragments of the internal standard (d10-phenanthrene) and then multiplied with the amount of internal standard (100 ng) and divided by the sample weight. This semiquantification was performed using the maximum peak area of each compound. Identification of Unknown Compounds. Compounds that exhibit a log−linear or nonmonotonic trend were identified using a library search (NIST MS library (2011)) in the first step. The suggested hits were manually evaluated by their spectral match, mass accuracy and LRI matching. Major sample spectrum features had to be explained and all major features in the libray specrum had to be present in the sample spectrum. Abundant ions had to be within 5 ppm mass error. In cases when an experimental LRI was available it had to be within 40 RI units. Predicted LRIs (NIST group contribution model29) had to be within the uncertainty range given. For compounds absent in NIST, molecular and fragment ion information obtained through normal 70 eV or low energy EI were used in subsequent steps: molecular formula generation

and application of the in silico fragmentation tool MetFrag30 using the ChemSpider database and allowing a 5 ppm error. Compounds that had higher data source or reference counts in ChemSpider were hereby preferred (data source and reference count were weighed 10% each, and spectral similarity was weighed 100%). In addition, possible molecular formulas were calculated for unidentified compounds using the exact mass of the suspected molecular ion. An example of the identification of compounds not present in NIST is given in the Results section. Compounds that remained unknown after these steps were labeled as such and included in the SI (Table S1).

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RESULTS AND DISCUSSION Reducing the Amount of Data. The amount of data was stepwise reduced, as shown in Figure 1. The initial step of the preselection includes all peaks that were detected and automatically aligned in the Guineu software. At this stage, tens of thousands of peaks (more than 22 000 peaks for PLE and more than 36 000 peaks for SPLE) are recognized. Only a few of these occur in the blanks above the set threshold. However, many components occur in only one of three replicates (mainly septum, column, and injector liner bleed) and, therefore, the replicate filter reduced the data size by an order of magnitude. Similarly, allowing only peaks that appear in data from at least four out of ten years also reduced the data size. In the last step, 75% and 70% of the peaks are removed from the PLE and SPLE data sets, respectively. Overall, the filtering yields a 97% and 98% size reduction of the PLE and SPLE data sets, respectively. The steps shown in Figure 1 start with data processing and end with time trend analysis. This part is quick and can be done in a day or two depending on how advanced the routines are. The most time-consuming step, however, follows then: the identification of compounds. The amount of time it takes to identify compounds depends on the number of compounds that need to be identified by manual interpretation or using in silico tools. In general, this step takes weeks rather than days and automated ways for, for example, in silico identification would speed up the process considerably. Monotonic trends. The analysis of the data revealed 192 compounds with log−linear trends below the significance level (106 for PLE and 107 for SPLE), where 60 and 132 exhibit increasing and decreasing trends, respectively. As the numbers show, the PLE and SPLE data sets overlap. In total, 21 compounds (i.e., 11 polycyclic aromatic hydrocarbons (PAHs) or PAH derivatives, five alkylbenzenes, three other hydrocarbons, trans-α-bergamotene, and 7-pentyl-bicyclo[4.1.0]heptane (a disinfection byproduct)) exhibit a log−linear trend and are detected through both methods. In all cases the trends 7815


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Table 1. Compounds with Trends with p-Values below the Nominal Significance Level (α = 0.05) Divided in Categories by Their Usea compound

log−linear (%)

smoother

semiquant. (μg/g)

specialty chemicals plastic additives tri(2-butoxyethyl) phosphate (TBEP) methylbiphenyl ethylbiphenyl 4-octyl-N-(4-octylphenyl) benzenamine diethyl phthalate 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-octylphenolg 1-methyl-3-nonylindane or 1-methyl-4-octyl-1,2,3,4tetrahydronaphthalene miscellaneous 1-(3-fluoropropyl)-4-(hexyloxy)-benzene compound ab compound bc 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

compound

log−linear (%)

smoother

semiquant. (μg/g)

consumer chemicals

−13 −13 −11 −6 −4 8 X X

−18 −10 −10 −8

X

X X

−10 −3 2 6 10 10 10 10 10 14 19 30

0.54 0.035 5.5

X X X

0.73

0.29 0.12 0.14 0.14 0.19 0.18 0.44 0.22 10 0.37

flavor and fragrances compound cd similar to tonalid galaxolide isomer galaxolide isomer β-caryophyllene galaxolide isomer humulene farnesyl acetone compound de compound ef,h tridecanal caryophyllene oxide 2,4-decadienal trans-α-bergamoteneh trans-β-ionone curcumeneh α-isomethyl ionone heptyl salicylate verdyl acetate calameneneh 6-methyl-5-hepten-2-one 3-octanone 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) PPCPs triclosan 7-pentyl-bicyclo[4.1.0]heptane 4-(3,4-dichlorophenyl) tetralone octocrylene homosalate

−13 4 4 4 5 6 6 6 6 6 7 7 7 8 9 10

X

X

16 24 145%

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 X X X X X X X X X X

19 20 29

−18 −9 8 19 41

4.4 0.39 5.1

X

0.30 0.11 0.53 11 1.1

a 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). bCompound a: 2-(2,3-Dihydro-1H-inden-1-yl)-1-(4-{[(E)-2phenylvinyl]sulfonyl}-1-piperazinyl)ethanone. cCompound b: 14,14-Bis(2-methylenecyclopropyl)-13,15-dioxa-14-siladispiro[5.0.5.3]pentadecane. d Compound c: 1-(1,3,4,4a,5,6,7-Hexahydro-2,5,5-trimethyl-2H-2,4a-ethanonaphthalen-8-yl)ethanone. eCompound d: (7a-Isopropenyl-4,5dimethyloctahydroinden-4-yl)methanol. fCompound e: 2-Isopropyl-5-methyl-9-methylene-bicyclo[4.4.0]dec-1-ene. gExtreme value in 2013. h Fragrance properties not described, but documented constituent of essential oil or plant extractives.

fragrance compounds and “other compounds”). The last of these classes (i.e., “other compounds”) includes all compounds that are incompatible with the other categories, including unidentified compounds. This group, i.e., “other compounds”, is also the largest group among compounds that exhibit increasing log−linear trends (Figure 2). In addition, this group accounts for a large fraction (17%) of decreasing log−linear trends (see Table 1 for the compounds comprising this group).

indicated by the two methods concur. The results of the log− linear regression of all compounds with significant monotonic trends are shown in Table S2. The compounds with significant trends are divided into seven classes, five of which are based on structural characteristics (PAH and PAH derivatives, alkylbenzenes, other hydrocarbons, steroidal substances, and aldehydes and ketones) and two diverse classes which are further specified in Table 1 (flavor and 7816


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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.

Figure 3. Time trend of homosalate, octocrylene, triclosan, 7-pentyl-bicyclo[4.1.0]heptane, 4-(3,4-dichlorophenyl) tetralone and 2,5-dichloroaniline. 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 p-value are shown. The number on the vertical axis indicates the concentration of the highest point from the semiquantification approach.

essential oils, perfume, and flavor material to Sweden increased

The largest group of the remaining compounds with increasing log−linear trends consist of flavor- and fragrancerelated compounds (see Table 1 for details). Use of natural substances in personal care products and imported values of

during the study period,31,32 thereby resulting in predominantly increasing trends for flavor- and fragrance-related compounds. 7817


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Environmental Science & Technology The remaining groups of compounds account for a rather small fraction (20%) of compounds with increasing trends. PAHs, hydrocarbons, and alkylbenzenes, account, collectively, for 70% of the decreasing trends (right-hand side Figure 2). Emissions of these compounds can be correlated with traffic. Although the number of cars using gasoline or diesel engines in the Stockholm area have increased,33 technological advancements may have resulted in the decreasing trends observed. Fuel consumption of vehicles per kilometer has decreased and combustion efficiency as well as catalyst performance have improved over time, resulting possibly in decreased PAH and alkylbenzene emissions. Interestingly, the only four PAH derivatives that exhibit an increasing trend are partly or completely saturated. In the discussion on monotonic trends for individual compounds we will first discuss the consumer chemicals and then the so-called specialty chemicals. Among the identified compounds in the group of consumer chemicals (Table 1) are several pharmaceuticals and personal care products (PPCPs). The strongest increasing trend (+41% per year) is observed for Homosalate, a UV filter (Figure 3). Another UV filter, Octocrylene, also exhibits a strong increasing trend (+19% per year, Figure 3). Both compounds are used in sunscreens and have both been previously (i.e., in 2009 and 2014) detected in sewage sludge from the STP in Stockholm, with concentrations being higher in 2014 than in 2009.34,35 In addition, usage of these chemicals has increased36,37 and, hence, explains the increasing trends. The semiquantification calculations indicate a higher concentration for Octocrylene (11 μg/g) than for Homosalate (1.1 μg/g). In Sweden maximum limits for concentrations of a few pollutants in sewage sludge are given for sludge that is intended for soil amendment. Those limits vary for different compound groups and are highest for nonylphenols and NPEs (100 μg/g). A comparison shows that Octocrylene and Homosalate concentrations exceed the limit for PCBs (0.4 μg/g) while Octocrylene also exceeds the limit for PAHs (3 μg/ g) and toluene (5 μg/g). The PPCPs category also consists of disinfectants. Among the disinfectants, Triclosan has exhibited a decreasing trend in sewage sludge collected from several different STPs across Sweden.14 Similarly, in this work, Triclosan is characterized by a steep decrease of 18% per year (Figure 3). A significant decreasing log−linear trend (−9% per year) is also observed for 7-pentyl-bicyclo[4.1.0]heptane (Figure 3), which is probably a disinfection byproduct. This work represents the first-ever report of 7-pentyl-bicyclo[4.1.0]heptane in environmental samples Finally, a medium strong increase (+8% per year) is observed for 4-(3,4-dichlorophenyl) tetralone (Figure 3), an impurity in, as well as a metabolite of, the pharmaceutical Sertraline.38,39 The increasing trend of 4-(3,4-dichlorophenyl) tetralone may be associated with increased use of Sertraline in the Stockholm area between 2006 and 201540 (Figure S1). The semiquantification calculations indicate that 4-(3,4-dichlorophenyl) tetralone is present in the sludge in concentrations up to 0.53 μg/g dry sludge, i.e., similar to the threshold of PCBs. Another group of compounds that is linked to the group of PPCPs are flavor and fragrance compounds. Many of these compounds, as for example musk related compounds, including Galaxolide, are used in perfumes and other personal care products. Those synthetic musks, including isomers and impurities,41,42 show similar increasing trends here in sewage sludge, ranging from +4% to +6% (Table 1). Similarly, use of Galaxolide in Sweden has been increasing from 2005 to 2013.43

Furthermore, a general increase in concentrations of synthetic musks in the environment over time, particularly in the marine environment, has been observed.44,45 The largest increase among flavor and fragrances was obtained for Verdyl acetate, an odor agent used in cleaning products but also personal care products (Table 1). The increase cannot be explained by an increased use. The registered use of Verdyl acetate in Sweden between 2005 and 2014 was almost constant.43 Other flavor and fragrance compounds that were found with significant increasing monotonic trends are trans-β-ionone, farnesyl acetone, humulene (also called α-caryophyllene), βcaryophyllene, and caryophyllene oxide, with medium strong increasing trends ranging from 5% to 9% per year. Only for βcaryophyllene and trans-β-ionone use in Sweden is reported. The number of preparations using caryophyllene and trans-βionone has been increasing during the studied period46 which is reflected in the increasing time trend. Among the identified consumer chemicals were also three food constituents, which were not in the NIST library and had to be identified using the nontarget identification workflow. Molecular formula generation using the apparent molecular ion resulted in tentative formulas C17H28O, C19H32O, and C19H34O. Further evaluation of fragmentation patterns using MetFrag with ChemSpider as structure database indicated that the compounds might be alkyl/alkenyl-substituted furans, specifically avocadenofuran, 2-(1-pentadecenyl)furan and 2pentadecylfuran. Those belong to the so-called avocadofurans, which are, as the name implies, natural constituents of avocado. EI spectra were obtained using literature searches and were found to match those of the sludge contaminants (Table S3). The three avocadofurans had similar increasing trends ranging from 18% to 25% per year; consistent with a common source, possibly avocado. The avocado import to Sweden increased by 17% per year over the period 2006 to 2013,47 i.e., mirroring the sludge trends. Many of the specialty chemicals (Table 1) are used as plastic additives. Diethyl phthalate and tri(2-butoxyethyl) phosphate (TBEP; Figure S2) exhibit decreasing time trends of −4% and −13% per year, respectively, concurring with the general decline of use between 2005 and 2015.46 In addition, decreasing trends of −13% and −11% are observed for two short chain alkyl biphenyls, one methylated and the other ethylated or dimethylated, respectively. Alkylated biphenyls are commonly used as plasticizers in industrial applications.48−50 The last plastic additives considered, 4-hexadecylbiphenyl, and 4-octylN-(4-octylphenyl)-benzenamine, exhibit an increasing and a decreasing monotonic trend, respectively, of +8% and −6% per year. Documented use of 4-hexadecylbiphenyl as a plasticizer is lacking, but (as previously mentioned) alkylated biphenyls are commonly used as plasticizers.48−50 The use of 4-octyl-N-(4octylphenyl)-benzenamine in plastic and rubber products and occurrence in environmental samples have previously been reported.51 The decreasing trend observed in this work is consistent with decreased 4-octyl-N-(4-octylphenyl)-benzenamine usage between 2005 and 2015.43 The specialty chemicals also include several compounds related to surfactants. Four of these compounds are nonylphenol isomers. These are most likely used for the same purpose and, hence, the decreasing trend observed in each case is unsurprising. The compound 4-tert-octylphenol is also part of this group. However, unlike the aforementioned four compounds, this compound exhibits a significant nonmonotonic 7818


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Environmental Science & Technology trend and, hence, will be discussed in the next section. Both, octylphenols and nonylphenols, are used as dispersing or emulsifying agents52 and in the production of surfactants (nonylphenol and octylphenol etoxylates). Similar to the nonylphenol trends found in this work, the use of NPEs and nonylphenols in Sweden decreased between 2005 and 2015.46 Among the remaining specialty chemicals only one, 2,5dichloroaniline, which is used as a pesticide43 and in the production of dyes and pigments,53 exhibits a strong increasing trend (in this case, + 30% per year (Figure 3)). This compound was previously reported in sewage sludge samples,54 but import statistics for Sweden are unavailable. Alkyl phenyl ester carbonates exhibit a medium strong increase (ca. + 10% per year). These compounds can be extracted from coal and may be used in industrial production processes.55 Other occurrences of these compounds have, to our knowledge, not been reported. Other compounds in this category are mainly used in small scale (academic) organic synthesis studies, and the reason for their occurrence in the sludge remains unclear. Finally, compounds that belong to the group of unknown chemicals remain to be identified. Emphasis is placed on compounds characterized by strong increasing trends, i.e., Unknown 7, Unknown 8, and Unknown 11. A first evaluation indicates that Unknown 7 has the molecular formula C16H33N3O4S and Unknown 8 has the molecular formula C24H31NO4. More information about these compounds and their spectra can be found in the SI (Tables S1 and S4 and Figures S3−S18). Within this group, the steepest trend is observed for Unknown 11 (+150% per year). This dramatic increase is rather uncommon but can happen shortly after a new chemical has been released on the market. Trends that are significant by chance may also show an extreme slope. This effect is related to the so-called magnitude error (m-error).56,57 Furthermore, the compound may be volatile and, hence, evaporate, or is unstable and degrades during storage. In general, degradation and evaporation should play a minor role since samples are freeze-dried before storage and stored in sealed containers, in the dark, at low temperatures. However, some evaporation can still occur for semivolatile sample constituents,58 and, moreover, some enzymes may still be active at low temperatures and can biodegrade sample constituents. Some biogenic compounds are readily biodegradable, e.g. fatty acids (through beta-oxidation) and small petroleum constituents such as aliphatics, naphthalene and alkylated naphthalenes.59 Anthropogenic compounds with similar structures may also be vulnerable to enzymatic degradation. In case of Unknown 11, the EI spectrum (Figure S13) indicates a low molecular weight (M+ at m/z 136) and, therefore, losses via evaporation must be considered. Nonmonotonic Trends. In addition to log−linear trends, nonmonotonic trends are evaluated using a smoother function.27 In total, 120 different compounds with significant nonmonotonic trends are detected, 43 in the PLE data set and 95 in the SPLE data set. If a compound exhibits a significant log−linear trend and a significant nonmonotonic trend, then the smoother describes the trend better than the log−linear curve. In total, 46 compounds (incl., 14 hydrocarbons, eight PAHs, eight alkylbenzenes, six natural substances, tridecanal, two alkyl phenyl carbonates, one nonylphenol, and 7-pentylbicyclo[4.1.0]heptane) exhibit both types of trends. The largest group of compounds characterized by nonmonotonic time trends are hydrocarbons, which account for almost 50% of the compounds (Figure 4). Many of these

Figure 4. Number of significant nonmonotonic 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.

hydrocarbons exhibit a similar trend: a decrease observed in the initial period followed by an increase for the later part of the time series. A large share (21%) is covered by PAHs and alkylbenzenes, which are, in principle, also hydrocarbons. More than half of these PAHs and alkylbenzenes exhibit a decreasing log−linear trend at the same time while the remaining compounds seem to be mostly decreasing as well, but do not exhibit a significant log−linear trend. The group of other compounds (Table 1) covers a similar share as the alkylbenzenes (10%). The remaining groups (aldehydes and ketones, flavor and fragrances and steroids) account for relatively small shares. Flavor and fragrance related compounds are one of the groups with fewer nonmonotonic trends. This group has a share of 10% among nonmonotonic trends. Tridecanal not only exhibits a significant nonmonotonic trend but also a significant log−linear trend. All remaining flavor and fragrances have a similar trend: the concentration increases until the middle of the studied period and then decreases again. This indicates a similar use pattern for those compounds. However, only for Lilial (Figure S19) and 3-carene, two perfuming agents, use statistics for Sweden are available that can confirm this trend and have a similar peak in the middle of the studied period.43 One of the constituents of the “other compounds” group is 4tert-octylphenol. Octylphenol is used (i) as an intermediate in the production of phenolic resins, which are used in the production of rubber and inks and (ii) in the production of surfactants (octylphenol etoxylates). The concentration of this compound in the sludge increases up to 2013 and decreases thereafter (Figure 5). In 2011, octylphenol was added to the REACH convention and classified as a “substance of very high concern”. In reaction to this classification, manufacturers may have started searching for replacements in case that octylphenol is banned soon. This could explain the post-2013 decreasing trend of this compound. The maximum concentration observed in the current study, 0.73 μg/g, is much below the threshold for nonylphenols and NPEs (100 μg/g) in soil amendment in Sweden. In the group of plastic additives, two phthalates are characterized by a nonmonotonic trend (Figure 5). Both phthalates elute in the range of dinonyl phthalates during the GC run (1tR). The separation on the nonpolar first dimension stationary phase is performed on the basis of boiling points and, 7819



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Article

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b01126. Part of the methods including materials, trend data for all log−linear trends, specific information for unknown compounds (spectra and spectra interpretation), and trends for additional compounds in comparison with their use (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Phone: +46 90 786 5171; e-mail: [email protected] (C.V.).

Figure 5. Time trend of octylphenol and two different phthalates. 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 semiquantification approach. The extreme value for 4-tert-octylphenol in 2013 is indicated by a bold black line around the circle.

ORCID

Cathrin Veenaas: 0000-0002-6368-6412 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the Swedish Museum of Natural History and especially Ylva Lind for providing the samples from the Environmental Specimen Bank. We would like to thank the Swedish EPA for funding through their Environmental Screening Program (Contract 2219-13-002).

hence, the unknown phthalates are most likely diisononyl phthalate (DINP) isomers. Similar to the time trend for the two phthalates shown here in Figure 5, use of DINP in Sweden decreased after 2010,43,46 likely because of increased use of nonphthalate plasticizers. In addition, one of the two phthalates was present at a rather high concentration (5.5 μg/g) around year 2010, but its concentration has fortunately dropped by a factor of 3 until 2015. Finally, 1-methyl-3-nonylindane or 1-methyl-4-octyl-1,2,3,4tetrahydronaphthalene (technical impurities of linear alkyl benzene commercial mixtures60 which are used to produce linear alkyl benzenesulfonates; LAS) is present in the sewage sludge, characterized by a nonmonotonic trend (Figure S20). Both compounds, 1-methyl-3-nonylindane and 1-methyl-4octyl-1,2,3,4-tetrahydronaphthalene, have never been previously reported in environmental samples. Outlook. The measures taken for reducing the amount of data and revealing compounds of potential concern have proven effective. The total number of peaks detected via the PLE and SPLE method was reduced by 97% and 98%, respectively. In addition, the time-trend analysis revealed many compounds, which were characterized by increasing log−linear trends. Moreover, many significant nonmonotonic trends were detected. However, nonmonotonic trends are (in general) of greater importance when longer time spans, than those considered in this work, are analyzed. To complete the comprehensive screening and time-trend analysis, an LCHRMS-based method has also been developed. This method will be applied to the same samples including a similar datareduction strategy as presented here. The GC×GC-HRMS and LC-HRMS data sets are expected to be complementary and, hence, cover a large part of the chemical-property space. Both sets of data will be stored in a digital archive and used in retrospective analysis. Altogether, this will provide new powerful tools for time-trend studies, or spatial-trend studies, which can reveal significant changes in use and emissions of well-known, emerging and new chemicals in society.

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REFERENCES

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