Development of the Model Train
The SOLUTIONS Model Train (SMT) consists of four building blocks: (a) simulation of emissions [5], (b) simulation of fate and transport [6] (c) characterisation of the mixtures’ risk for aquatic ecosystems [7], and (d) the prediction of substance properties based on their molecular structure [8]. SMT simulates the emissions, fate and transport, and mixture toxic pressure as a function of space and time, related to the variability of weather, hydrology, wastewater management infrastructure, etc. The model provides fully quantitative outputs, i.e. spatio-temporal data on exposure and on the magnitude of risk (mixture toxic pressure). SMT operates on the scale of Europe or for individual European river basins. The spatial schematisation as well as the hydrology, temperature, soil type, land use and crop cover are derived from the pre-existing Europe-wide hydrology model E-Hype [9]. The model domain for Europe-wide simulations includes 22,728 sub-catchments, with an average size of 252 km2 (Fig. 1).
Concentrations of chemicals and stress on aquatic systems on EU scale
After a smaller scale exercise for pharmaceuticals in Sweden [10], we calculated the emissions and concentrations of 1785 chemicals on the scale of the EU. Figure 1 shows the computational domain, consisting of all river basins covering parts of the 28 EU countries, Norway and Switzerland. Figure 2 shows an example of the simulated emissions to surface waters of the pharmaceutical Fluconazole (CAS 86386-73-4; one of the 1785 chemicals). Figure 3 shows an example of the simulated concentrations in surface waters of the same chemical. The 1785 simulated chemicals include 1348 chemicals of various uses, extracted from REACH registration dossiers, 105 pharmaceuticals and 332 pesticides. They are a subset of 5100 chemicals with quantified emissions, for which sufficient degradability [11] and toxicity data [7] are already available. In addition, the mixture toxic pressure of these 1785 chemicals on aquatic communities was derived from simulated time-variable bioavailable concentrations. The result was converted to one overall map showing a classification of the mixture toxic pressure to diagnose sites with probably insufficient protection in line with Water Framework Directive guiding principles (Fig. 4). Note that for the remaining 3315 chemicals, current Predicted Environmental Concentrations may serve to identify chemicals that possibly occur in high concentrations and need to be prioritised for toxicity assessment. This study only considered direct effects of chemical exposure to effect endpoints such as growth and reproduction. Specific effects, such as endocrine disruption, were not addressed.
The validation of simulated concentrations [5] showed that their accuracy is not perfect, often associated to the limited availability of key input data (see “Requirements”). For 226 validation cases, the simulated concentrations were correct on average, with possible significant under- or overprediction for individual substances: for 65% of cases the error was within one order of magnitude, while for 90% of cases the error was within two orders of magnitude. This should be seen in a context of concentrations of chemicals spanning up to 16 orders of magnitude, and toxicity data spanning up to 9 orders of magnitude. Thus, the models can still provide a meaningful image of the expected impact, variable in space and time. The models can also cover a large number of substances. For these reasons, the models can supplement monitoring data for the diagnosis of current occurrence of and effects from chemicals and can provide a prognosis of the changes thereof as a result of socio-economic changes or the implementation of abatement measures. The below results illustrate this.
Differences between river basins
The assessment of the model-derived data, both input and output, allowed for an analysis of differences between European river basins [12]. Which basins are the most affected? What factors are responsible? In a broad sense, the simulated chemicals’ pressure in different river basins is determined by the pressure from population centres and economic activities (including agriculture and industry), relative to the dilution capacity of the surface water system. The highest effects are therefore encountered in relatively small river basins, if they happen to be highly developed and densely populated. An example of the latter is the Llobregat basin in Spain (≈ 5000 km2, including the city of Barcelona).
Analysis of hotspots
The assessment of model-derived data also allows for an analysis of hotspots of high mixture toxic pressures—likely associated with high impacts on ecological status [see Policy Brief MARS-SOLUTIONS]—within river basins [12]. These hotspots are found in water systems of densely populated areas throughout Europe, such as Lisbon, Madrid, Valencia, Barcelona, Athens, the western part of the Netherlands, Essen-Dortmund, Brussels, Paris, St Petersburg and Belgrade.
Ranking of substances
After model applications for individual substances (PFOS, PFOA, [13, 14]), toxic risks to aquatic ecosystems of 1785 chemicals produced in Europe have been simulated and potential drivers of mixture toxicity have been identified [12]. This exercise provided a spatially variable picture, especially for pharmaceuticals and pesticides, due to differences in the use intensity between EU countries. On a European scale, the substances expected to be the most relevant regarding ecological impacts via direct effects on vital traits such as growth and reproduction (out of the 1785 we analysed) were identified. Among these were the commercial chemicals octamethylcyclotetra-siloxane (CAS 556-67-2), dodecan-1-ol (CAS 112-53-8) and anthraquinone (CAS 84-65-1), as well as the fungicide chlorothalonil (CAS 1897-45-6). A similar assessment was done for different individual river basins. On such smaller spatial scales, however, the results get more sensitive for the availability of reliable regional information about the use intensity of chemicals.
Ranking of sites and substances in a context of uncertainty
Sites and substance ranking based on predicted environmental concentrations (PECs) is sensitive to details of the methodology applied and to the uncertainty of the PECs. Ranking based on measured environmental concentrations (MECs) is sensitive to the available sampling stations and sampling times and to the accuracy of the laboratory analytical methods. Both approaches are sensitive to the method and data used for toxicity evaluation of the studied compounds. Consequently, sites and substances cannot and should not be ranked in absolute terms but can be classified, for example in a traffic light fashion:
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Site or substance is expected to present a risk (“red”)
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Site or substance is not expected to present a risk (“green”)
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Site or substance cannot be classified in the above categories (“yellow”).
The latter group needs more information to arrive at a conclusion, while they can still be ranked according to the likeliness to be “red” or “green”.
Cost-effective abatement
The SOLUTIONS approaches and models have been used to test the efficacy of end-of-pipe measures in the wastewater chain to alleviate effects in surface waters [15, 16]. We demonstrated this in the Rhine Basin Case Study, first by evaluating the changes brought about by extra wastewater treatment throughout the basin, to evaluate the potential effect of such measures. By limiting the end-of-pipe measures to those sources with the highest contribution to the effects, a higher return-on-investment can be expected. In one example, about 70% of the maximum reduction of mixture toxic pressure was achieved by extra treatment of only 20% of the emission sources. Such a high return-on-investment was found only if a spatially differentiated water quality improvement was pursued: for example, improvement only in areas where drinking water is abstracted, or only at the basin outlet to protect the receiving marine waters.
Future scenarios
The SOLUTIONS models have been used to investigate the effects of expected trends in the use of chemicals towards the year 2030. One of such trends is the expected increased use of pharmaceuticals because of the ageing of the population. Based on the assumptions made, the simulation results indicated that the pressure from this substance group would increase by 36% [8]. The scenario simulations also pointed out that the phasing out of substances of very high concern (SVHC), listed on the REACH Candidate List, can have a strong positive effect on water quality, whilst regrettable substitution (substitution by equally harmful substances) can be identified via modelling, and therefore, avoided. Candidate List substances include important groups of chemicals (e.g. plasticisers). The results show that regulation can have a high impact on the reduction of emissions of problematic chemicals [17] and is an important element for the transition to a more sustainable chemistry [18].
