Published in Environment International, 91: 301-311
4.4.2 Implications for research and policy
Consequently, while nested multimedia box models may adequately predict between cell variation in chemical concentrations, they do not lend themselves for the identification of local hot-spots. Instead, once a set of priority APIs is identified in a grid cell, a spatially more detailed local study could be performed to identify such local hot-spots.
Removal of APIs during primary and secondary sewage treatment was estimated with the SimpleTreat 4.0 model [196], and distributions as derived by Franco et al. [204] were assigned to the parameters of the model to take uncertainty in this removal into account (Table A4.3). Per iteration, one removal fraction was calculated per API, and used for all STPs throughout Europe that apply such treatment. Similarly, per API one European-wide value was used for the removal efficiency of tertiary treatment techniques. Thus, removal efficiencies were considered uncertain, but not variable between STPs. In reality, however, variability between STPs in their capacity to remove the same API can be considerable
[e.g., 194]. Consequently, spatial variability in the environmental impact of APIs might have been underestimated in our study.
4.4.2 Implications for research and policy
A grid cell-specific approach might be highly relevant for a meaningful and efficient local prioritisation of APIs. Indeed, it enables national and regional regulators to decrease the risk of misguided allocation of resources to APIs of no local environmental concern.
Conversely, it allows to focus risk management options to where they may be expected to be most effective. If a PAF of 1% would be considered a threshold of environmental concern, oxytetracycline and erythromycin should be selected as priority APIs in some regions in the United Kingdom (Fig. 4.1, panels IA and IIA). Moreover, it is important to notice that tetracyclines are not only consumed by humans, but are also extensively used in the veterinary setting [152]. While a limitation of oxytetracycline and erythromycin consumption in these regions would be most effective to reduce the environmental impact, current European legislation does not provide a regulatory basis for such source-based options. Indeed, end-of-pipe management is still the main regulatory option to reduce potential environmental risks of APIs. Instead, voluntary source-based actions might be sought. For example, environmental considerations might be taken into account by physicians in their choice between otherwise equivalent pharmaceuticals, using the previously developed decision support tool [210]. When taking uncertainty into account,
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cefuroxime and ciprofloxacin are also included in the set of priority APIs. The largest contributor to uncertainty of these APIs is by far the estimation of their toxicity parameters (Figs. 4.4C and 4.4D). Further research into their aquatic toxicity could largely decrease the uncertainty in the model predictions, and provide insight into whether cefuroxime and ciprofloxacin should indeed be included as priority APIs.
We found that spatial differences in PAFP95 values of individual APIs are larger than spatial differences in PAFP50 values (Figs. 4.1 and A4.1). In each environmental grid cell, PAFP50 values depend on the most likely values of the chemical-specific input parameters only. PAFP95 values, however, derive from both the most likely values of the chemical-specific input parameters, as well as the uncertainty range in these parameters.
Additionally, different input parameters might be relevant in different environmental grid cells. For example, parameters related to the removal efficiency of a certain sewage treatment technique are only relevant in locations where this technique is actually applied.
This complicated interplay between spatially varying characteristics and uncertain input parameters results in more unique local sets of priority APIs when parameter uncertainty is taken into account. As a consequence, the maximum set of priority APIs, which comprises all unique local sets of priority APIs, contains a larger portion of the total amount of APIs assessed.
The choice of a threshold PAF can be very influential for the selection of priority APIs and therefore also for the justification of using a location-specific approach. Because species sensitivity distributions are largely conceptual constructs in which reality is simplified, justification of the choice of a threshold PAF based on its ecological relevance is difficult. Zijp et al. [211] aimed to address this through the setting of boundary conditions for chemical pollution footprints based on (acute) EC50-based SSDs. They proposed a methodology for the derivation of both ecosystem vulnerability based boundaries (i.e., natural boundaries), and chemical management based boundaries (i.e., policy boundaries). Natural boundaries were derived from food web data models as described by Mulder et al. [212]), resulting in a threshold PAF of 3%. Policy boundaries were derived from (chronic) NOEC-based SSDs, extrapolating the 95%-protection criterion generally used in chemical management policies to their (acute) EC50-based equivalent. This resulted in a threshold PAF value of 0.1%.
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Here, we selected threshold PAF values of 1% and 0.1%, but these were mainly chosen for illustrative purposes. We therefore shortly assessed the influence of using threshold values higher than 1% and 0.1%. This assessment showed that a location-specific approach would still be worthwhile at threshold PAF values as high as 7.5%: this value is still exceeded by the PAFP95 of one single API (oxytetracycline) in one single environmental grid cell (the region of London). Finally, the level of confidence required for the prioritisation influences the range of relevant threshold values. Here, we selected the 95th percentile of the uncertainty distribution of PAF as its upper confidence limit, but a higher percentile can also be chosen when more confidence is desired. For example, when using the 99th percentile of the uncertainty distribution of PAF as its upper confidence limit, minimum SRFs of 2/7 and 1/9 are reached at threshold PAF values of 1% and 0.1%, respectively.
4.5 CONCLUSIONS
The inclusion of spatially explicit information enables a more efficient local environmental prioritisation of APIs in Europe, compared to a non-spatial EU-wide approach. When parameter uncertainty is taken into account in the assessment, the added value of this approach remains, and in some cases even increases due to the interplay between spatially varying parameters on the one hand and uncertain parameters on the other.
Indeed, probabilistic spatially explicit environmental models can add a lot of pertinent information, from highlighting both basic data uncertainties that could be decreased by generating additional experimental data, to uncertainties within the model, a better understanding of which allows an improved characterisation of the confidence that can be attributed to the model predictions. On a more practical level, spatially explicit models can pinpoint both priority APIs and also priority grid cells, providing guidance for further in-depth investigation and risk management measures.
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4.6 ACKNOWLEDGMENTS
This research was funded by the European Commission, partly through FP7 project PHARMAS (contract no. 265346), and partly through the project TOX-TRAIN (contract no. 285286) under the Industry-Academia Partnerships and Pathways (IAPP 2011).
4.7 APPENDICES
Appendices related to this chapter can be accessed online via doi:10.1016/j.
envint.2016.01.025 or http://www.ru.nl/environmentalscience/staff/individual-staff/
oldenkamp/.
A4. Uncertainty distributions and additional results