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Clearly, however, it is not sufficient to rely on individual technical inventions to meet the challenges outlined above. Malaj et al. This finding is the result of how we deal with missing values when assessing monitoring data. In risk assessment, detected concentrations of preselected chemical s are compared with their environmental quality standard EQS.

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EQS are derived using worst case assumptions and the concept of an overall threshold. Thus, while on the chemical exposure side, missing detections are generally ignored i.


The latter approach of dealing with uncertainty is adopted from prospective chemical risk assessment. Here, the less we know about the adverse effects of a contaminant, the more caution i. Our current assessment may, thus, be severely confounded due to a bias in data generation, where only a few pre-selected chemicals are monitored and assessed against laboratory-based toxicity information. Furthermore, the highly complex structural biological parameters used to assess an ecological status are monitored independently and are not considered in relation to contaminant exposure.

In this setting, it remains difficult to identify contamination as a causative factor for an insufficient ecological status or to identify sources or drivers of adverse effects. To better link the information already available with reasonable additional efforts in the context of chemical contamination and ecological status assessments, we aimed to develop more balanced approaches for exposure and effect monitoring of freshwater quality.

As laid out in Altenburger et al. Moreover, we suggested developing distinct solution-oriented monitoring strategies. The three strategies developed here are designed to 1 identify compounds of concern for specific river basins, 2 assess ecological impact of contamination across different sites, and 3 establish causal relationships between chemical contamination and biological effects. Each of the strategies builds on specific combinations of information from chemical and biological analyses, with a primary focus on organic contaminants. The suggestions laid out here document a synthesis of many different ideas and studies.

We, thus, intend to provide guidance for policy makers and water resource managers, demonstrating improved strategies to deal with mixtures of pollutants in water resource management.

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Within the WFD context, members are obliged not only to monitor certain agreed contaminants across all EU member states so-called priority substances but also to identify pollutants of regional or local importance and monitor these eventually as river basin-specific pollutants RBSPs [ 53 ]. These procedures depend on the availability of suitable data both on the exposure and effect side, which often remains fragmentary.

Significant progress in dealing with data gaps has been achieved through an initiative of the NORMAN association [ 19 ] which suggests a framework to cope with uncertainty, and thus assists the consideration of compounds with incomplete data sets [ 67 ].


Nonetheless, the identification of RBSPs depends on the availability of suitable chemical analytical methodologies, which are able to demonstrate the occurrence of contaminants at biologically relevant concentrations, i. Development of improved, cost-efficient methodologies is key to overcome these current limitations. In SOLUTIONS, we focused on passive sampling methods that provide estimates of time-weighted average freely dissolved concentrations of trace organic compounds, and high-volume sampling techniques that provide simultaneous access to chemical and bioanalytical analysis, and development of multi-residue methods of higher sensitivity.

These sampling techniques provide improved exposure estimates that can subsequently be used in conjunction with available effect information for biological quality elements BQEs: fish, macroinvertebrates, phytoplankton, macrophytes to identify the specific toxicological relevance of contaminants. Using passive sampling methods to identify RBSPs during chemical monitoring offers several advantages for biological exposure characterization.

Firstly, passive sampling can provide time-integrated information about specific aquatic pollutants over extended time periods several weeks—months. This is a more realistic reflection of aquatic organism exposure in surface waters, compared to grab sample analysis unless monitoring a discrete pollution event, where grab sampling may be more suitable. Secondly, passive samplers provide a measure of freely dissolved concentrations, rather than total concentrations.

Freely dissolved concentrations are thought to be more comparable with single-chemical effect concentrations for aquatic species from laboratory studies used in risk assessment for aquatic environments , due to their proportionality to the chemical potential and chemical activity [ 54 ].

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Finally, passive sampler concentrations, after equilibration with sampled media, allow for a direct comparison of chemical levels in various compartments, thus helping to assess the compartmental distribution and to consider source and sink relationships, as well as to study accumulation and magnification of chemicals in aquatic biota.

A novel mobile dynamic passive sampling approach was introduced, which is applicable for characterizing chemical pollution along large rivers, lakes or sea transects, providing samples with chemical patterns integrated in time and space. A case study on the combination of passive sampling, multi-residue analysis, and bioassays demonstrated the occurrence of out of priority and emerging pollutants in the river Bosna Bosnia and Herzegovina [ 65 ].

The city of Sarajevo was identified as a major source of pollution, with downstream samples showing significant responses in all bioassays. While the estrogenic activity was largely explained by the specific estrogens measured, the drivers of the other observed effects remain largely unknown.

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Combining chemical and bioanalytical techniques to characterize water samples shows great potential for identifying pollutants of potential concern more coherently, as component-based effect assessment can be compared with effect observations in the same sample [ 55 ]. Current limitations mostly relate to the amount of sample required for biological analysis, which typically involves multiple bioassays and enriched samples [ 1 ]. To overcome the logistical challenges associated with providing access to hundreds of liters of water samples, a novel, automated-solid phase extraction LV-SPE device was developed and tested for organic compound recoveries [ 60 ].

Good recoveries were observed for more than compounds exhibiting a wide range of physico-chemical properties. Moreover, the generated extracts proved suitable for biotesting using various in vitro and in vivo bioassays [ 50 ], with effect recovery observations similar to those for chemical recovery for LV-SPE [ 51 ].

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The device was used in various case studies comparing chemical and bioanalytical findings, to study how much of an observable effect in freshwater might be explained through chemical analysis using a bioanalytical equivalent concentration BEQ approach e. The identification of RBSPs is desirable for chemical monitoring. Analytical methods to detect contaminants in low concentrations in water have improved progressively over time.

A review of water contaminant detection performed at the onset of the SOLUTIONS project demonstrated that over organic compounds were detected in European freshwaters, but that the overlap in the compounds analyzed was low due to the different multi-compound methods used [ 15 ]. Only 13 of the compounds found were analyzed in all of the seven studies reviewed. Thus, it is currently difficult to tell whether reports on specific compound detections are site or method specific or both.

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  6. To improve transparency and allow for more rational choice and comparison of methods, we compiled standard operating procedures SOPs for more than water contaminants. These SOPs were in two main forms. These SOPs which were designed to simplify the identification and application of the developed methods by other research groups and laboratories, and to facilitate the effective monitoring and control of the targeted compounds, are compiled in a publicly available SOLUTIONS deliverable [ 71 ].

    Kuzmanovic et al. The analytical efforts mentioned above are restricted to target chemicals that are either known or suspected to occur and for which analytical standards are commercially available. To extend the universe of chemicals considered during water monitoring for illustration see Fig. The figure follows the concept of Ternes et al. Daily non-target monitoring of Rhine river water revealed its potential to support monitoring, revealing significant time series of unknown compounds at high intensities, for which the structures were subsequently elucidated and ultimately identified as industrial contaminants Fig.

    A major bottleneck hindering the widespread use of non-target analysis in RSPB identification results from the currently time-consuming and limited means of identifying compound structures out of the data-rich and complex mass spectral information. The workflow has been applied in several case studies, including the formation and elimination of transformation products through wastewater treatment including ozonation and several post-treatment steps [ 59 ].

    The effectiveness of the advanced treatment and comparison of different post-treatment steps could already be demonstrated by bulk characterization parameters such as peak numbers or overall reduction in mass. The results of effect-based tools supported these conclusions regarding the effectiveness of treatment. The rapid progress in technology and distribution of powerful LC— and GC—HRMS technology for comprehensive NTS, open data repositories for digital freezing of samples together with the increasing availability of tools for big data evaluation and pattern analysis will pave the way for a more comprehensive assessment of chemical contamination in the near future.

    An approach is needed to identify bioactive candidate chemicals in complex mixtures that may be relevant on a larger spatial scale [ 24 , 35 ]. The goal of a methodology that has been labeled virtual effect-directed analysis vEDA [ 24 ] is to assist explaining of biological effects by reducing the complexity of mixture components via multivariate statistics and pattern recognition methods on large sample numbers using a decomposition approach.

    This approach is able to handle peaks from non-target analysis, and thus is not restricted to previously known chemicals. Virtual EDA helps to identify peaks that co-vary with observed biological effects, suggesting these as candidate causative chemicals Fig. Obviously, this approach does not directly provide cause—effect relationships, but allows hypothesis generation, which must be confirmed using, e.

    Successful vEDA generally requires that:. Adapted from Brack et al.

    Workflow for virtual effect-directed analysis to reduce mixture complexity and to identify candidates that emerge from multi-site data correlation analysis. The observed effect is caused by a limited small number of toxicants among those present in the samples. Sufficient variance larger than the data uncertainty of the observed effect and chemical composition patterns occurs across the different samples.

    A case study on a time series of mutagenic wastewaters from a mixed industrial and municipal WWTP serves as an example. Varying levels of mutagenicity were detected at different time points over approx. Applying partial least squares analysis, the number of peaks of interest to explain the variability in mutagenicity was reduced to about signals [ 35 ].


    The overrepresentation 30 times larger of nitrogen-containing compounds among the selected peaks, along with enhanced mutagenicity in a diagnostic Ames Salmonella stem YG suggested aromatic amines as drivers of mutagenicity. After specific derivatization techniques were applied [ 44 ], several of these compounds could be identified. The intensity of two peaks, in fact two diaminophenazine isomers, were found to correlate with mutagenicity and were eventually confirmed as the drivers of the observed mutagenicity [ 45 ].

    Operationally, this has been separated into the assessment of both a chemical and ecological status, which complicates management actions aiming to reduce the impact of major drivers of degradation [ 11 ]. Any approach to identify ecological impacts caused by chemical contamination on community composition has to overcome this divide. Furthermore, approaches need to discriminate the impact of toxic chemicals from non-chemical stressors, which often have a strong impact on community composition.

    Starting from the contamination perspective, analytically undetected but toxicologically relevant compounds, transformation products and mixture effects may be overlooked in an approach that is purely based on target chemical measurements.