General discussion
8.2 CONCLUSIONS AND RECOMMENDATIONS
a large inventory of a wide range of chemicals. These distributions were used to infer the toxicity of a mixture containing data-poor chemicals. The interchemical variability present in the distribution, and thus the extrapolation uncertainty, was smaller when a more specific subset of the inventory was used for its characterisation. At the same time, however, sampling uncertainty was larger in these distributions, because less chemicals were available for the characterisation of their parameters.
In conclusion, a balance has to be
found between specificity and data availability, to optimally reduce extrapolation and sampling uncertainty. Finally, the aquatic risk assessment study described in Chapter 7 showed that optimal use of all available data can substantially reduce the uncertainty in the results of the assessment. Through the use of Bayesian hierarchical modelling, information at different levels of aggregation can be used to decrease rather than increase uncertainty, especially when little data are available on the subject of interest.
8.2 CONCLUSIONS AND RECOMMENDATIONS
The novel methodologies and tools developed within this thesis provide a means to perform informed probabilistic human and aquatic risk assessments. The spatially explicit fate and exposure tools, as described in Chapters 2-5, can be combined with probabilistic methodologies for effect and hazard assessment, as described in Chapters 6 and 7. Additionally, the probabilistic framework from Chapter 4 enables the identification of the most important sources of uncertainty and spatial variability in the potentially affected fractions of species (PAFs) throughout Europe. The uncertainty in species sensitivity distributions (Chapter 8), introduces most uncertainty in these PAFs, while
FIGURE 8.1
Sources of variability functioning as source of uncertainty in risk assessment. Colours represent different types of variability.
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consumption volumes and the presence of certain sewage treatment techniques explain most of the spatial variability in PAF values found throughout Europe. Similarly, the probabilistic framework from Chapter 7 enables the identification of the most important sources of uncertainty and interindividual variability in the human dose distributions from which human exposure limits can be derived. For ciprofloxacin and methotrexate, the two drugs included in the case study in Chapter 7, most uncertainty is introduced during the extrapolation steps covering sub-chronic/sub-acute to chronic toxicity, and covering interspecies differences in toxicity.
The human health risk assessment performed in this thesis showed that the risks due to environmental exposure can be considered negligible, at least for the set antibiotics and anticancer drugs assessed. Indeed, probabilistically derived human exposure limits for ciprofloxacin and methotrexate (Chapter 6) are 6-7 orders of magnitude larger than the highest worst-case exposure estimations derived for environmental grids in Europe (Chapter 2). Similar conclusions on human health risks due to environmental exposure to human pharmaceuticals have been drawn by others [e.g., 29, 67, 68]. It must be noted, however, that the human exposure limits probabilistically derived for ciprofloxacin are based on its chondrotoxicity, and that stricter deterministic exposure limits have been derived based on its microbiological toxicity, i.e., its effect on the human intestinal flora
[32, 310]. Nevertheless, these conservative deterministic exposure limits are still 3-5 orders of magnitude larger than the highest worst-case exposure estimated. Nevertheless, as already mentioned in Section 8.1.1.1, the human health risk assessment performed in this thesis was limited to direct effects after human use, due to exposure to the parent compound. It is recommended that future research on human health risks due to environmental exposure to antibiotics includes the assessment of indirect effects, i.e., the development of antibiotic resistant pathogens and subsequent environmental transport and human exposure, not only resulting from human but also from non-human antibiotics use, e.g., veterinary and aquaculture use. Moreover, future human health risk assessment methodologies should be developed that include the identification, fate and effect of human metabolites as well as transformation products that might arise during wastewater treatment and drinking water purification.
Contrary to human health risks, potential freshwater ecosystem risks due to exposure to antibiotics and anticancer drugs cannot be considered negligible. In certain densely
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populated regions in Europe, up to 3% of the aquatic species are potentially affected by one or more human antibiotics present in the surface waters, based on EC50 values derived from short-term toxicity tests (Chapter 4). Detrimental chronic and subtle effects have been shown to occur in the environment for other human pharmaceuticals, e.g., (semi-) synthetic oestrogens affecting reproductive organs in male fish [65], the anti-anxiety drug oxazepam influencing fish behaviour [443], and the anti-depressant fluoxetine decreasing territorial aggression in coral reef fish [444]. Consequently, it is recommended that existing pharmaceuticals, so-called legacy APIs, are subjected to a spatially explicit screening of their potential environmental risks. The large amount of legacy APIs (more than 4,000 currently in use [35]) necessitates an approach that requires relatively little data, balancing complexity and capacity, and that includes a thorough uncertainty analysis. When aquatic risks are considered undesirable, end-of pipe solutions might seem to be most logical at first glance, because of the societal benefits of pharmaceuticals. However, this would generally imply upgrading sewage treatment plants (STPs), the expected costs of which are extremely high. For example, the upgrade of all STPs in the United Kingdom in order to reach proposed European environmental quality standards for (semi-)synthetic hormones costs an estimated £13 to £15 billion pounds [445]. Thus, other measures should be explored relating to the start rather than the end of the pipe, e.g. the inclusion of environmental considerations in prescription practice as proposed and described in Chapter 3. Other initiatives relate to the development process of new pharmaceuticals. So-called benign-by-design or re-design principles can be applied to make pharmaceuticals more susceptible to environmental (bio)degradation while retaining their therapeutic potency [446]. Also, pharmacological properties measured during the development of new pharmaceuticals might be used to make an early assessment of their environmental fate possible [447, 448]. Indeed, it is recognised that knowledge in the field of human pharmacology can be of importance for environmental risk assessment of pharmaceuticals, for example through the evaluation of the potential existence of human drug targets in non-human species [381].
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TABLE 8.1 Examples of uncertainty and variability quantitatively assessed throughout this thesis, with reference to the describing chapters. UncertaintyExampleChapter(s) Model uncertaintyRelative impact ratios can be derived for all environmental grids in Sweden, or based on calculations at the national level.3 Two different models available for the description of dose-response relationships in experimental animals6 Legitimacy of using lognormal and gamma distributions to describe interchemical variation in toxicity7 Using the correct set of hyperparameters to parameterise non-informative prior distributions7 Multiple different subsets of chemicals from a chemical inventory might be used to derive multi-substance PAF distributions7 Measurement uncertaintyCharacterisation of physicochemical properties of APIs4 Characterisation of environmental degradation of APIs4 Sampling uncertaintyExcretion as parent compound after consumption, based on limited amount of individuals4, 5 Removal by tertiary sewage treatment techniques, based on limited amount of sewage treatment plants (STPs)4 Mean response per experimental dose group, based on limited amount of test animals6 Characterisation of variability in toxicokinetics, based on limited amount of individuals6 Characterisation of variability in toxicodynamics per Adverse Outcome Pathway (AOP), based on limited amount of chemicals6 Chemical-specific mean aquatic toxicity, based on limited amount of species7 Chemical-specific interspecies variation in toxicity, based on limited amount of species7 Extrapolation uncertaintyRatio of inpatient and outpatient per capita consumption, extrapolated from other countries4 Characteristics of primary and secondary treatment steps, extrapolated from other European sewage treatment plants (STPs)4 Selection of local set of priority APIs from Europe-wide set of priority APIs4 API-specific ratio between sub-chronic/sub-acute and chronic mammalian toxicity, extrapolated from other chemicals6 API-specific difference in sensitivity between median test organism and median human, extrapolated from other chemicals6 AOP-specific variability in toxicodynamics, extrapolated from other AOPs6 API-specific mean aquatic toxicity, extrapolated from other chemicals7 API-specific interspecies variation in aquatic toxicity, extrapolated from other chemicals7
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VariabilityExampleChapter(s) Spatial variabilityConsumption of APIs in EU Member States2, 4, 5 Compliance to pharmaceutical prescription in EU Member States2, 3, 4 Disposal behaviour of leftover APIs in EU Member States2, 3, 4 Connectivity of human population to STPs throughout Europe2, 3, 4, 5 Removal techniques applied in STPs throughout Europe2, 3, 4 Sewage sludge disposal practice in EU Member States2, 3, 4 Environmental characteristics of 100 * 100 km2 grids throughout Europe2, 3, 4 Hydrological characteristics of 5 * 5 Arc minutes grids throughout Europe5 Relative contributions of groundwater and surface water to total drinking water in EU Member States2, 3 Food and drinking water consumption in EU Member States2, 3 Temporal variabilityMonthly climate conditions throughout Europe 5 Interspecies variabilityDifference in sensitivity between aquatic species4, 7 Difference in sensitivity between experimental animals and humans6 Interindividual variabilityFood and drinking water consumption for different age groups2 Diving behaviour for different age groups2 Ingestion of soil particles for different age groups2 Toxicokinetics for different individuals and different age groups6 Toxicodynamics for different individuals6 Interchemical variabilityAPI-specific consumption in EU Member States2, 4, 5 Defined Daily Dose (DDD) per API3 API-specific excretion as parent compound after consumption2, 3, 4, 5 Removal efficiency of specific APIs in STPs2, 3, 4, 5 Physicochemical properties of APIs2, 3, 4, 5 Environmental degradation of APIs2, 3, 4, 5 Aquatic toxicity of APIs2, 3, 4, 7 Mammalian toxicity of APIs2, 3