Kathrin Fenner

The Challenge of Micropollutants in Aquatic Systems

The increasing worldwide contamination of freshwater systems with thousands of industrial and natural chemical compounds is one of the key environmental problems facing humanity. Although most of these compounds are present at low concentrations, many of them raise considerable toxicological concerns, particularly when present as components of complex mixtures. Here we review three scientific challenges in addressing water-quality problems caused by such micropollutants. First, tools to assess the impact of these pollutants on aquatic life and human health must be further developed and refined. Second, cost-effective and appropriate remediation and water-treatment technologies must be explored and implemented. Third, usage and disposal strategies, coupled with the search for environmentally more benign products and processes, should aim to minimize introduction of critical pollutants into the aquatic environment.

Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence

T increased availability of high resolution mass spectrometry (HR-MS) in chemical analysis has dramatically improved the detection and identification of compounds in environmental (and other) samples. This has opened up new research opportunities in environmental sciences, demonstrated by over 200 research papers per year, increasing strongly (source: SCOPUS keywords “high resolution mass spectromet”, subject “envi”). The elucidation of small molecules such as emerging pollutants and their transformation products using HR-MSbased suspect and nontarget analysis is gaining in relevance, also in other fields (e.g., metabolomics, drug discovery, forensics). However, confidence in these HR-MS-based identifications varies between studies and substances, since it is not always possible or even meaningful to synthesize each substance or confirm them via complementary methods (e.g., nuclear magnetic resonance). These varying levels of confidence are very difficult to communicate to readers concisely and accurately. In Figure 1 we propose a level system, which arose from intense discussions within our department, to ease the communication of identification confidence and form the basis of further discussions on this topic. This level system is not intended to replace guidance documents (e.g., EU Guideline 2002/657/EG), but specifically covers the new possibilities in HR-MS-based analysis. Our discussion started with the levels published by the Metabolomics Standards Initiative (MSI), as we experienced many cases that fitted “in between” their proposed levels. While Jeon et al. first refined these levels, these were tailored to the specific investigation. The levels in Figure 1 reconcile differences in the two proposals, contain additional levels pertinent to screening methods and are clarified in the text below.

Evaluating Pesticide Degradation in the Environment: Blind Spots and Emerging Opportunities

The benefits of global pesticide use come at the cost of their widespread occurrence in the environment. An array of abiotic and biotic transformations effectively removes pesticides from the environment, but may give rise to potentially hazardous transformation products. Despite a large body of pesticide degradation data from regulatory testing and decades of pesticide research, it remains difficult to anticipate the extent and pathways of pesticide degradation under specific field conditions. Here, we review the major scientific challenges in doing so and discuss emerging opportunities to identify pesticide degradation processes in the field.

Recent advances in environmental risk assessment of transformation products

When micropollutants degrade in the environment, they may form persistent and toxic transformation products, which should be accounted for in the environmental risk assessment of the parent compounds. Transformation products have become a topic of interest not only with regard to their formation in the environment, but also during advanced water treatment processes, where disinfection byproducts can form from benign precursors. In addition, environmental risk assessment of human and veterinary pharmaceuticals requires inclusion of human metabolites as most pharmaceuticals are not excreted into wastewater in their original form, but are extensively metabolized. All three areas have developed their independent approaches to assess the risk associated with transformation product formation including hazard identification, exposure assessment, hazard assessment including dose-response characterization, and risk characterization. This review provides an overview and defines a link among those areas, emphasizing commonalities and encouraging a common approach. We distinguish among approaches to assess transformation products of individual pollutants that are undergoing a particular transformation process, e.g., biotransformation or (photo)oxidation, and approaches with the goal of prioritizing transformation products in terms of their contribution to environmental risk. We classify existing approaches for transformation product assessment in degradation studies as exposure- or effect-driven. In the exposure-driven approach, transformation products are identified and quantified by chemical analysis followed by effect assessment. In the effect-driven approach, a reaction mixture undergoes toxicity testing. If the decrease in toxicity parallels the decrease of parent compound concentration, the transformation products are considered to be irrelevant, and only when toxicity increases or the decrease is not proportional to the parent compound concentration are the TPs identified. For prioritization of transformation products in terms of their contribution to overall environmental risk, we integrate existing research into a coherent model-based, risk-driven framework. In the proposed framework, read-across from data of the parent compound to the transformation products is emphasized, but limitations to this approach are also discussed. Most prominently, we demonstrate how effect data for parent compounds can be used in combination with analysis of toxicophore structures and bioconcentration potential to facilitate transformation product effect assessment.

High-Throughput Identification of Microbial Transformation Products of Organic Micropollutants

During wastewater treatment, many organic micropollutants undergo microbially mediated reactions resulting in the formation of transformation products (TPs). Little is known on the reaction pathways that govern these transformations or on the occurrence of microbial TPs in surface waters. Large sets of biotransformation data for organic micropollutants would be useful for assessing the exposure potential of these TPs and for enabling the development of structure-based biotransformation prediction tools. The objective of this work was to develop an efficient procedure to allow for high-throughput elucidation of TP structures for a broad and diverse set of xenobiotics undergoing microbially mediated transformation reactions. Six pharmaceuticals and six pesticides were spiked individually into batch reactors seeded with activated sludge. Samples from the reactors were separated with HPLC and analyzed by linear ion trap-orbitrap mass spectrometry. Candidate TPs were preliminarily identified with an innovative post-acquisition data processing method based on target and non-target screenings of the full-scan MS data. Structures were proposed following interpretation of MS spectra and MS/MS fragments. Previously unreported microbial TPs were identified for the pharmaceuticals bezafibrate, diazepam, levetiracetam, oseltamivir, and valsartan. A variety of previously reported and unreported TPs were identified for the pesticides. The results showed that the complementary use of the target and non-target screening methods allowed for a more comprehensive interpretation of the TPs generated than either would have provided individually.

Fate of β-blocker human pharmaceuticals in surface water: comparison of measured and simulated concentrations in the Glatt Valley Watershed, Switzerland.

This study focused on the occurrence and fate of four beta-blockers (atenolol, sotalol, metoprolol, propranolol) in wastewater and surface water. Measured concentrations were compared with predicted concentrations using an implementation of the geo-referenced model GREAT-ER for the Glatt Valley Watershed (Switzerland). Particularly, the question was addressed how measured and simulated data could complement each other for the exposure assessment of human pharmaceuticals and other micropollutants entering surface water through wastewater treatment plants (WWTP). Concentrations in the Glatt River ranged from <LOQ to 83 ng L(-1) with the highest concentrations found for atenolol. Higher loads were measured on days with combined sewer overflow events during high flow conditions. GREAT-ER was able to predict spatially resolved river concentrations based on average consumption and excretion data, removal in wastewater treatment plants (WWTPs) and dissipation and degradation processes in surface water within a factor of 2. These results indicate that modelling might be sufficient to estimate daily average exposure concentrations for compounds that are either recalcitrant or whose degradation and sorption behaviour can be predicted with confidence based on laboratory experiments. Chemical measurements, in contrast, should be reserved for assessing point sources, investigating mechanisms which lead to short-term temporal fluctuations in compound loads, and determining in-situ degradation rates in conjunction with modelling.

Micropollutant biotransformation kinetics associate with WWTP process parameters and microbial community characteristics.

The objective of this work was to identify relevant wastewater treatment plant (WWTP) parameters and underlying microbial processes that influence the biotransformation of a diverse set of micropollutants. To do this, we determined biotransformation rate constants for ten organic micropollutants in batch reactors seeded with activated sludge from ten diverse WWTPs. The estimated biotransformation rate constants for each compound ranged between one and four orders of magnitude among the ten WWTPs. The biotransformation rate constants were tested for statistical associations with various WWTP process parameters, amoA transcript abundance, and acetylene-inhibited monooxygenase activity. We determined that (i) ammonia removal associates with oxidative micropollutant biotransformation reaction rates; (ii) archaeal but not bacterial amoA transcripts associate with both ammonia removal and oxidative micropollutant biotransformation reaction rates; and (iii) the activity of acetylene-inhibited monooxygenases (including ammonia monooxygenase) associates with ammonia removal and the biotransformation rate of isoproturon, but does not associate with all oxidative micropollutant biotransformations. In combination, these results lead to the conclusion that ammonia removal and amoA transcript abundance can potentially be predictors of oxidative micropollutant biotransformation reactions, but that the biochemical mechanism is not necessarily linked to ammonia monooxygenase activity.

A tiered procedure for assessing the formation of biotransformation products of pharmaceuticals and biocides during activated sludge treatment.

Upon partial degradation of polar organic micropollutants during activated sludge treatment, transformation products (TPs) may be formed that enter the aquatic environment in the treated effluent. However, TPs are rarely considered in prospective environmental risk assessments of wastewater-relevant compound classes such as pharmaceuticals and biocides. Here, we suggest and evaluate a tiered procedure, which includes a fast initial screening step based on high resolution tandem mass spectrometry (HR-MS/MS) and a subsequent confirmatory quantitative analysis, that should facilitate consideration of TPs formed during activated sludge treatment in the exposure assessment of micropollutants. At the first tier, potential biotransformation product structures of seven pharmaceuticals (atenolol, bezafibrate, ketoprofen, metoprolol, ranitidine, valsartan, and venlafaxine) and one biocide (carbendazim) were assembled using computer-based biotransformation pathway prediction and known human metabolites. These target structures were screened for in sludge-seeded batch reactors using HR-MS/MS. The 12 TPs found to form in the batch experiments were then searched for in the effluents of two full-scale, municipal wastewater treatment plants (WWTPs) to confirm the environmental representativeness of this first tier. At the second tier, experiments with the same sludge-seeded batch reactors were carried out to acquire kinetic data for major TPs that were then used as input parameters into a cascaded steady-state completely-stirred tank reactor (CSTR) model for predicting TP effluent concentrations. Predicted effluent concentrations of four parent compounds and their three major TPs were corroborated by comparison to 3-day average influent and secondary effluent mass flows from one municipal WWTP. CSTR model-predicted secondary effluent mass flows agreed within a factor of two with measured mass flows and confidence intervals of predicted and measured mass flows overlapped in all cases. The observed agreement suggests that the combination of batch-determined transformation kinetics with a simple WWTP model may be suitable for estimating aquatic exposure to TPs formed during activated sludge treatment. Overall, we recommend the tiered procedure as a realistic and cost-effective approach to include consideration of TPs of wastewater-relevant compounds into exposure assessment in the context of prospective chemical risk assessment.

The University of Minnesota pathway prediction system: predicting metabolic logic

The University of Minnesota pathway prediction system (UM-PPS, http://umbbd.msi.umn.edu/predict/) recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions, and predicts transformations of these groups based on biotransformation rules. Rules are based on the University of Minnesota biocatalysis/biodegradation database (http://umbbd.msi.umn.edu/) and the scientific literature. As rules were added to the UM-PPS, more of them were triggered at each prediction step. The resulting combinatorial explosion is being addressed in four ways. Biodegradation experts give each rule an aerobic likelihood value of Very Likely, Likely, Neutral, Unlikely or Very Unlikely. Users now can choose whether they view all, or only the more aerobically likely, predicted transformations. Relative reasoning, allowing triggering of some rules to inhibit triggering of others, was implemented. Rules were initially assigned to individual chemical reactions. In selected cases, these have been replaced by super rules, which include two or more contiguous reactions that form a small pathway of their own. Rules are continually modified to improve the prediction accuracy; increasing rule stringency can improve predictions and reduce extraneous choices. The UM-PPS is freely available to all without registration. Its value to the scientific community, for academic, industrial and government use, is good and will only increase.

pH-Dependent Sorption of Acidic Organic Chemicals to Soil Organic Matter

Due to their increased polarity, many contemporary biologically active chemicals exhibit acid functions and may thus dissociate to their anionic conjugated base at pH values typically present in the environment. Despite its negative charge, soil organic matter (SOM) has been demonstrated to be the main sorbent in soils, even for the anionic species of organic acids. Nevertheless, few data exist that allow for a systematic interpretation of the sorption of organic acids into SOM. Therefore, in this study, the sorption of the neutral and anionic species of 32 diverse organic acids belonging to nine different chemical groups to SOM was investigated. Partition coefficients were determined from HPLC retention volumes on a column packed with peat, at three Ca(2+)-concentrations and over a pH range of 4.5-7.5. The influence of Ca(2+)-concentrations on anion sorption was small (factor 2 in the usual environmental Ca(2+)-concentration range) and independent of molecular structure. Generally, the organic carbon-water partition coefficients, K(oc), of both the neutral and anionic species increased with increasing molecular size and decreased with increasing polarity. At an environmentally relevant Ca(2+)-concentration of 10 mM, the investigated anions sorbed between a factor of 7-60 less than the corresponding neutral acid. This factor was more homogeneous within a group of structurally related compounds. These results indicate that while similar nonionic interactions seem to govern the partitioning of both the neutral and anionic species into SOM, the electrostatic interactions of the anionic species with SOM are a complex and currently not well understood function of the type of acidic functional group. The HPLC-based, flow through method presented in this study was shown to yield consistent results for a wide range of organic acids in a high-throughput manner. It should therefore prove highly useful in further investigating how different acidic functional groups affect anion sorption to SOM.