Zhe
Li
*a,
Emma
Undeman
b,
Ester
Papa
c and
Michael S.
McLachlan
a
aDepartment of Environmental Science and Analytical Chemistry (ACES), Stockholm University, 10691 Stockholm, Sweden. E-mail: zhe.li@aces.su.se; Tel: +46 8 674 7188
bBaltic Sea Centre, Stockholm University, 10691 Stockholm, Sweden
cQSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, 21100 Varese, Italy
First published on 14th February 2018
The removal efficiency (RE) of organic contaminants in wastewater treatment plants (WWTPs) is a major determinant of the environmental impact of these contaminants. However, RE data are available for only a few chemicals due to the time and cost required for conventional target analysis. In the present study, we applied non-target screening analysis to evaluate the RE of polar contaminants, by analyzing influent and effluent samples from a Swedish WWTP with direct injection UHPLC-Orbitrap-MS/MS. Matrix effects were evaluated by spiking the samples with isotope-labeled standards of 40 polar contaminants. For 85% of the compounds, the matrix effects in the influent and effluent were not significantly different. Approximately 10000 compounds were detected in the wastewater, of which 319 were identified by using the online database mzCloud. Level 1 identification confidence was achieved for 31 compounds for which we had reference standards, and level 2 was achieved for the remainder. RE was calculated from the ratio of the peak areas in the influent and the effluent from the non-target analysis. Good agreement was found with RE determined from the target analysis of the target compounds. The method generated reliable estimates of RE for large numbers of contaminants with comparatively low effort and is foreseen to be particularly useful in applications where information on a large number of chemicals is needed.
Environmental significanceOrganic contaminants are constantly discharged from wastewater treatment plants into surface waters. The removal efficiency of organic contaminants in wastewater treatment plants is a major determinant of the environmental impact of these contaminants. Our manuscript shows that non-target analysis, combining a direct injection method and state-of-the-art high-resolution mass spectrometry, can be used to efficiently and reliably estimate removal efficiency for a large number of polar organic contaminants with comparatively low effort. Our work illustrates an easy and simple concept for overcoming the data limitations that have hampered our efforts to understand contaminant behavior. For instance, the method creates exciting new research opportunities to generate QSARs for predicting removal efficiency and new possibilities for regulators to prioritize contaminants for up-stream control of emissions. |
State-of-the-art high-resolution mass spectrometry (HRMS) techniques using full scan mode have opened new possibilities in environmental analysis. One of them is to screen for both known and unknown/unexpected compounds within one run.12–14 Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been widely applied to characterize emerging contaminants in wastewater and its impacted systems.15–18 HRMS-based screening approaches are able to provide extensive information on the components present in a sample. The combination of a highly accurate exact mass calculation with MS/MS information is often sufficient for assigning a chemical identity to a peak. Therefore a reference standard is not always required as in target analysis.19–21 Since unbiased non-target screening generates enormous datasets and the data processing can be time- and resource-consuming, dedicated approaches for data reduction and compound prioritization are encouraged.22 One strategic approach is to study the changes in the chemical composition of an environmental matrix by assessing the differences between samples collected at different points in space or time. This approach can be employed to study chemical fate processes in the environment. It has been used to identify contaminant transformation products,13,14,23–25 but there are few reports of this strategy being employed to investigate other processes.18,26–29
In the present study, we further explored the process-oriented applications of non-target analysis by using this technique to evaluate the overall RE of organic contaminants in WWTPs. Combining a direct injection method and LC-Orbitrap-HRMS, we acquired full scan datasets of sampled wastewater influent and effluent from a municipal WWTP. We carried out a robust matrix effect test and assessed the potential of taking the difference in the signal strengths of the identified chemicals to estimate their RE. Complementary target analysis was carried out for 42 target compounds in order to: (1) evaluate the ability of the non-target screening approach to identify relevant contaminants in these matrices and (2) compare the non-target RE results obtained from peak area ratios with the quantitative data.
Two sample preparation methods, direct injection and solid phase extraction (SPE), were employed to explore differences in the non-target screening results arising from sample enrichment. For direct injection, triplicated influent and effluent samples (1 mL) were spiked with the isotope-labeled standard mixture (50 ng absolute amount for each compound) before the pH was adjusted to neutral using a sulfuric acid solution (0.3 M) and a sodium hydroxide solution (1 M). Each sample was then filtered directly into a glass LC-vial using a 0.45 μm PTFE syringe filter.
The SPE method was based on Kern et al. (2009), a method which has been well documented and widely applied in suspect and non-target screening analysis.21,26–29 Briefly, both influent and effluent samples (100 mL) were spiked with the isotope-labeled standard mixture (50 ng absolute amount for each compound) before filtration through glass fiber filters (GF/F; 0.47 μm; Whatman, Brentford, UK). To enrich compounds with a broad range of physical–chemical properties, self-packed two-layer cartridges were used containing 200 mg Oasis HLB (Waters, Milford MA, USA) as a top layer and a mixture of 150 mg Isolute ENV+ (Biotage AB, Uppsala, Sweden), 100 mg Strata-X-CW cation exchanger and 100 mg Strata-X-AW anion exchanger material (Phenomenex, Torrance, CA, USA) as a bottom layer. The two layers were separated by a PE frit (Supelco, Bellefonte, PA, USA). The conditioning of the cartridges was performed using 5 mL methanol followed by 10 mL Milli-Q water. The samples were extracted at a flow rate of approximately 10 mL min−1. The cartridges were then completely dried under vacuum for 1 h before they were eluted first with 6 mL of a freshly prepared basic solution of ethyl acetate:methanol (50:50) containing 0.5% ammonia, and then with 3 mL of an acidic solution of ethyl acetate:methanol (50:50) containing 1.7% formic acid. The final pH of the extract was neutral. The sample extracts were evaporated under a gentle nitrogen stream at a temperature of 35 °C to 100 μL (after rinsing the glass wall twice with 200 μL of methanol). The 100 μL extract was reconstituted with 900 μL Milli-Q water, vortexed and then filtered through a 0.45 μm PTFE syringe filter into a glass LC-vial. All prepared samples were stored frozen until analysis.
(1) |
Since quantitative determination is not possible within the context of non-target analysis for unknown chemicals and for chemicals without reference standards, we tested the possibility of using the peak area as an indicator of chemical abundance by conducting the matrix effect tests as described above. If the influence of the matrix on the signal of a chemical is similar between influent and effluent, eqn (1) can then be simplified to:
(2) |
The non-target workflow was simultaneously applied to WWTP influent and effluent samples. For all the identified compounds, eqn (2) was applied to estimate RE as a non-target approach. Additionally, for the 42 compounds for which we had reference compounds, we also quantitatively determined their RE with eqn (1) using the concentrations from the target analysis. When the concentration of an analyte in effluent was <LOQ, this concentration was set as the respective LOQ. Uncertainty analysis was performed and is presented in the ESI.†
For all standards in all five matrices without enrichment, the relative standard deviation of the peak area (n = 9) was <21% (see Fig. 2). For 33 out of the 40 standards, the five matrices analyzed by direct injection had similar responses to Milli-Q water, with the relative response factors ranging from 0.75 to 1.25. The remaining 7 standards (atorvastatin-d5, bicalutamide-d4, climbazole-d4, fluoxetine-d5, glimepiride-d5, guanyl urea-15N4, and triclosan-d3) had a response that was markedly suppressed (by up to 85%) in the matrix samples as compared to the Milli-Q water. Ion suppression was observed in both influent and effluent, yet to different extents for four of the compounds (atorvastatin-d5, bicalutamide-d4, glimepiride-d5, and triclosan-d3). The 7 standards showing matrix effects did not group around specific retention times, so it was not possible to predict from retention time which compound would be affected by matrix suppression.
One-way ANOVA was performed to test whether the differences between the mean response in direct-injection influent and effluent were statistically significant for the tested compounds. The results indicate that for 34 out of the 40 compounds no statistically significant difference was found at a confidence level of 95%, which indicated that the matrix effects in influent and effluent were comparable for a large majority of the compounds. The six compounds (marked with an asterisk in Fig. 2) for which matrix effects differed significantly are 1H-benzotriazole-d4 and metformin-d6 under the ESI positive mode, and atorvastatine-d5, bicalutamide-d4, glimepiride-d5, and triclosan-d3 under the ESI negative mode. The ion suppression increased with increasing proportion of influent in the influent/effluent mixtures for 1H-benzotriazole-d4, metformin-d6, and triclosan-d3, while it decreased for atorvastatin-d5, bicalutamide-d4, and glimepiride-d5.
In contrast, for 28 compounds statistically significant differences were observed between the influent and effluent water that had been pre-concentrated on SPE columns (Fig. 2). Generally, most of the isotope-labeled standards had higher ion suppression in the SPE-enriched influent than in the effluent. We hypothesize this to be attributed to higher concentrations of interfering compounds in the SPE extracts. The results show that the data from direct injection are much more suitable for the estimation of RE from peak areas (eqn (2)) than the data from the samples that had been pre-concentrated on SPE columns.
RE was calculated from the direct-injection samples using the simplified method (eqn (2)) for all 319 compounds. Fig. 3 compares the RE of some of these compounds to the RE determined from the results of target analyses conducted at the same WWTP in the context of the Swedish screening programs for chemical contaminants.32 The Swedish screening program provided data for up to 40 influent samples and 89 effluent samples (24 h flow-proportional) collected and analyzed during the period of 2005–2009. For all of the compounds common to both datasets, the uncertainty ranges (defined as mean ± standard deviation) of the RE estimated from this study and the RE calculated from the Swedish screening program overlap. This is evidence of the reliability of the non-target approach to estimating the RE of organic contaminants.
The non-target screening results also show consistencies with literature data. For instance, the two carbamazepine metabolites (carbamazepine-10,11-epoxide and 10,11-dihydro-10,11-dihydroxy-carbamazepine) were found in both influent and effluent; they have been widely reported in human blood samples, wastewater samples, and surface water systems.33–36 In addition, coupled parent-product occurrence was observed. One example is that the 50% removal of valsartan was accompanied by the formation of valsartan acid that was detected only in effluent. Other compounds present only in effluent that hence can be classified as newly formed, e.g., 2-methoxyestradiol, carboxyl-clopidogrel, and penicillic acid, are expected from the degradation of known precursors via reactions that occur during wastewater treatment such as methylation, carboxylation, and hydroxylation.
In total, out of the 319 identified compounds, 67 had negative RE values, corresponding to an increased concentration after wastewater treatment. This can result from the formation of transformation products during the wastewater treatment and/or the back-transformation of native compounds from the degradation of their conjugate metabolites. Two examples are given in the following section.
Target compoundb | Target analysis | Non-target analysis | ||
---|---|---|---|---|
Direct injection | SPE | Direct injection | SPE | |
a A plus sign indicates that the compound was detected/identified, while a minus sign indicates that the compound was not detected/identified. b Out of the 42 target compounds, 26 were detected in all cases and are not shown in the table. | ||||
2-Chlorobenzoic acid | + | + | − | − |
Anastrozole | − | − | − | − |
Atorvastatin | − | + | − | − |
Bezafibrate | − | + | − | + |
Chlorothiazide | − | + | − | + |
Chlorthalidone | − | − | − | − |
Climbazole | − | + | − | + |
Clofibric acid | − | − | − | − |
Fluoxetine | − | − | − | − |
Glimepiride | − | − | − | − |
Irbesartan | − | + | − | + |
MCPA | − | − | − | − |
Methotrexate | − | − | − | − |
Pravastatin | + | + | − | − |
Ranitidine | − | + | − | + |
Triclosan | + | + | − | − |
Fig. 4 Comparison of removal efficiency (RE, %) calculated using target analysis and eqn (1) to the RE for the same compounds estimated using non-target analysis and eqn (2). The comparison includes the results from the two sample processing methods (i.e., direct injection and SPE enrichment). Error bars represent standard deviation (n = 9). |
In general, there was good consistency in the detection of the compounds between the target and the non-target analysis. The six compounds that were not detected in the target analysis using direct injection were also not detected by the non-target approach using direct injection. However, three compounds (2-chlorobenzoic acid, pravastatin, and triclosan, see Table 1 and Fig. 4) were detected with target analysis but not with non-target analysis. These compounds can be considered false negatives of the non-target approach. They were present at concentration close to the LOD for target analysis, and the NLOD for non-target analysis was higher than the LOD for target analysis (Table S3†). The remaining compounds were all unequivocally identified using the non-target screening approach.
The concentrations of the detected compounds ranged from 0.062 μg L−1 for ranitidine to 270 μg L−1 for caffeine in the influent water, and from 0.056 μg L−1 for chlorothiazide to 4.7 μg L−1 for gabapentin in the effluent water (Table S4†). They agree well with the concentration ranges reported for a large variety of pharmaceuticals in influent and effluent from Swedish WWTPs.37–39 The concentrations of most of the detected compounds were higher in the influent water than in the effluent water, with two exceptions: irbesartan and chlorothiazide. For irbesartan the increase in concentration during treatment can be attributed to the decomposition of its conjugate metabolites. Chlorothiazide, on the other hand, may have been formed as the product of the transformation of another parent chemical. It has been shown that chlorothiazide can be generated from both hydrolysis and microbial degradation of the diuretic drug hydrochlorothiazide.13,40,41
RE was determined from the results of the target analysis for both the direct-injection samples and the SPE-enriched samples using eqn (1). The results are compared with the RE determined from non-target analysis using eqn (2) (Fig. 4 and S41†). For the target analysis, RE ranges from close to 0% for carbamazepine and oxazepam to 100% for acetaminophen and caffeine, which is in line with previous findings on the removal of these compounds in conventional WWTPs.37 For the 29 compounds that were detected by both direct injection and the SPE method, the average difference for RE between the two sample processing methods is 14% and the median difference is 4%.
Comparing the RE of the tested chemicals from the target and non-target analyses, there is good agreement between the two sets of data obtained using the direct injection method, with an average RE difference of 10% and a median difference of 3%. For all compounds but 1H-benzotriazole, no statistically significant difference was observed at a confidence level of 95%. The non-target RE of 1H-benzotriazole is statistically lower than the target analysis value by 10%. This can be attributed to the more pronounced matrix effect on 1H-benzotriazole in the influent than in the effluent (see Fig. 2), leading to an underestimation of RE by eqn (2). While a significant difference in the response factor was also observed for another detected compound, metformin, the RE difference between target and non-target analyses is only 3%. This can be explained by the high RE of metformin, as a consequence of which the difference in matrix effects between influent and effluent was less influential on RE.
In contrast to the good agreement for RE obtained from the target and non-target analysis data using the direct injection method (Fig. S41†), poor agreement was found for the data using the SPE method, with an average RE difference of 46% and a median difference of 38% (Fig. 4). A significant difference was found at a confidence level of 95% for 29 out of the 35 tested compounds. Negative RE values were obtained for nearly one third of the compounds using the SPE/non-target method. The underestimation of RE by this method is consistent with the matrix effect results which showed large differences in the response factors between influent and effluent for SPE-enriched samples. Hence the simplification of eqn (1) to eqn (2), which was used in the non-target method, was not valid for the data obtained from the SPE method. We explored whether the SPE/non-target data could be corrected with the SPE/target data by conducting correlation analysis between the retention time of each compound and the quotient of the SPE/target and SPE/non-target data. However, no correlation was found.
This study also demonstrates that direct injection is a valid and efficient approach to both target and non-target analyses of contaminants in WWTPs. False negatives occurred in our study with the non-target screening approach combined with the direct injection method due to the relatively low concentration levels of these compounds (2-chlorobenzoic acid, pravastatin, and triclosan). It may be possible to address this by using large volume injection to increase the injected chemical amount to achieve higher responses, but this might also increase the matrix background.
While direct injection clearly resulted in matrix effects for some chemicals (Fig. 2), it was found that for the majority of our tested compounds the influent and the effluent water from the WWTP were so similar in terms of matrix that it was possible to calculate RE with peak area ratios. PCA was performed to assess the uncertainty in extrapolating our findings of the matrix effect tests from the standards to the 319 identified compounds for which structures could be assigned. In total, 195 molecular descriptors were calculated from the molecular structure to visualize the representativeness of the 40 standards for the chemical domain of the identified compounds. The scores plots (Fig. S42†) of PC1 vs. PC2 and PC2 vs. PC3 provide a summary of the structural variation among the 359 compounds (i.e. 319 contaminants in addition to the 40 standards). Compounds with a similar molecular structure are located close to each other in the PCA space. According to the congenericity principle,44 structurally similar compounds are expected to give similar responses and therefore to behave similarly during wastewater treatment. The PCA results (Fig. S43†) show that for 275 of the identified compounds (about 85%) the chemical domain was well covered by the 34 isotope-labeled standards for which matrix effects did not differ between influent and effluent. This supports the applicability of the method to chemicals in the portion of the domain covered by the 34 standards. In total, 40 compounds, including all PEGs and PPGs and chemicals with long chains, were out of the chemical domain cover by the 34 standards. The RE calculated for these chemicals by eqn (2) may be less reliable than that for the majority of the dataset. Furthermore, the random distribution within this chemical domain of the six labeled standards (five of them showed ion suppression, see Fig. 2) with different matrix effects shows that the existence of a matrix effect cannot be predicted from PC1, PC2, or PC3. In summary, the extrapolation of our finding of similar matrix effects in influent and effluent is reasonable for a large portion of the chemical domain of the identified compounds, but within that domain we still expect that ∼15% of the chemicals will have different matrix effects. Additional studies are needed to explore the reproducibility of our findings for other WWTPs.
While all spiked standards were recovered by the workflow, there are chemicals outside the analytical method and screening approach domain. A major reason for chemicals not being identified is the instrument sensitivity, since peaks with too low intensity are discarded during the isotopic peak screening step. This can potentially be overcome by including an enrichment step prior to instrumental analysis, but the non-target results can be more subject to matrix effects as shown in this study, making complementary quantitative analysis using internal standards indispensable to ensure the accuracy. Furthermore, most enrichment procedures inevitably result in the discrimination of certain compounds, e.g., those that are not retained on a sorbent. Other compounds that fall outside the method domain are those that cannot be retained on the LC column and/or cannot be ionized in ESI. This limitation can potentially be addressed by implementing other separation methods and ionization techniques.
Despite the effectiveness and reliability of the presented screening approach, extrapolating our finding of equal response factors in influent and effluent for our labeled standards to all chemicals is a source of uncertainty. As a result of this uncertainty, it is advisable to use target analysis to assess the RE of specific chemicals. Nonetheless, the power of the presented method lies in its ability to estimate RE for a very large number of compounds with comparatively low effort and material requirements (e.g., no standards are needed for the chemicals studied), as compared to many traditional approaches using non-target screening analysis followed by developing target analytical methods. Our method is expected to be particularly useful in applications where RE values for a large number of chemicals are needed. Such applications could include:
- Evaluating the performance of existing WWTPs. In addition to a quality control function, the method could be applied to assess the influence of operating conditions, flow, temperature and other variables on WWTP performance.
- Studying how treatment technology influences the WWTP performance. For instance, the method could be used to compare the performance of WWTPs that employ different treatment technologies.
- Developing new wastewater treatment technologies. The method could play a key role in evaluating the effectiveness of the new technology.
- Generating quantitative structure–property relationships (QSPRs) to predict RE from chemical structures, thereby generating predictive capacity for the release of chemicals from the technosphere to the environment.
- Identification of chemicals for potential upstream management. Chemicals with low RE will be released to surface waters, where they are also more likely to be persistent. Although upgrading conventional WWTPs by using advanced treatment techniques (e.g., activated carbon and ozone) can potentially increase the RE of these chemicals, this may not be sufficient or cost effective. Upstream management of emissions to wastewater may be the best option for such chemicals.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7em00552k |
This journal is © The Royal Society of Chemistry 2018 |