Priority setting

4.3 RESULTS

4.3.1 Priority setting

For oxytetracycline and erythromycin, the most likely PAF (PAF_P50) exceeds a value of 1% in one or more environmental grid cells (Fig. 4.1, panels IA and IIA, respectively), while for the other seven pharmaceutical ingredients the PAF_P50 remains below 1%

for all grid cells. Hence, these two APIs would be considered for prioritisation at the European level, if no parameter uncertainty would be taken into account. At a threshold PAF of 0.1%, cefuroxime and ciprofloxacin would also be considered (Fig. 4.1, panels IIIB and IVB, respectively), extending the set of priority APIs to four. The locations of concern are typically limited to grids in the United Kingdom (e.g., the regions of London and Manchester), and Romania (e.g., the region of Bucuresti), and the probabilities of a PAF over 1% or even 0.1% are in general much lower than 50% in other grid cells.

Consequently, a grid cell-specific approach would achieve 100% reduction of the set of priority APIs in the vast majority of the environmental grid cells. Indeed, 99% of the environmental grid cells have an SRF of 1 at a threshold PAF of 1% (Fig. 4.2, panel IA). At a threshold PAF of 0.1%, this is 94% of the environmental grid cells (Fig. 4.2, panel IB).

This emphasises the advantage of following a location-specific prioritisation of these nine legacy APIs based on a spatially explicit EU-wide modelling approach.

When prioritisation is based on the upper confidence limit of the uncertainty distribution of PAF (PAF_P95), the maximum set of priority APIs at a threshold PAF of 1%

increases to four (i.e., oxytetracycline, erythromycin, cefuroxime, ciprofloxacin; Fig. 4.1), and to all nine APIs at a threshold PAF of 0.1% (Figs. 4.1 and A4.1). Logically, the inclusion of parameter uncertainty in the prioritisation leads to a lower amount of environmental grid cells where a 100% reduction in the set of priority APIs can be reached (Fig. 4.2).

Nevertheless, SRFs are still substantial for the majority of the environmental grid cells, especially in Scandinavian countries such as Sweden. Even at a threshold PAF of 0.1%, 96% of the environmental grid cells have an SRF > 0.5 (Fig. 4.2, panel IIB).

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4.3.2 Parameter importance analysis

The parameter importance analysis with Spearman’s rank correlation coefficients showed that spatial variability in PAF values for oxytetracycline is mostly influenced by country-specific outpatient and inpatient per capita consumption (Fig. 4.3A). Indeed, large variation in the per capita consumption of oxytetracycline exists between European countries, with the United Kingdom being the largest per capita consumer by far. Erythromycin and ciprofloxacin, and to a lesser extent cefuroxime, have a relatively even distribution of per capita consumption volumes throughout Europe compared with oxytetracycline. The spatial variability of PAF values for these three APIs is mainly influenced by population density and related characteristics (i.e., the number of STPs present in a specific grid cell and the surface area of urban soil) (Fig. 4.3B-D). Additionally, the extent to which STP influents are subjected to specific tertiary sewage treatment techniques is relevant for the spatial variability of PAF values of oxytetracycline and erythromycin (Fig. 4.3A-B).

Grid cell-specific characteristics related to mobility in soils are relevant for the spatial variability of PAF_P50 values for cefuroxime and ciprofloxacin (i.e., agricultural soil surface area, carbon content of the soil, and soil erosion) (Fig. 4.3C-D). Finally, between-cell variability in surface water volume (i.e., surface water depth and area) explains a substantial part of the spatial variation in PAF_P50 values for ciprofloxacin (Fig. 4.3D).

The uncertainty distributions of the PAF with an upper confidence limit (PAF_P95) larger than 1% were also subjected to a parameter importance analysis. This analysis showed that uncertainty in toxicity parameters mainly contributes to the uncertainty in PAFs (Fig 4.4). For the four priority APIs, the large majority of the uncertainty in PAFs stems from uncertainty in both the interspecies spread in sensitivity, with average contributions ranging from 69% to 77%, and the average sensitivity of species, with average contributions ranging from 7% to 21%. Other input parameters contribute relatively little to the uncertainty of PAFs (Fig. 4.4).

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FIGURE 4.1

The spatial distribution of the probabilities for oxytetracycline (I), erythromycin (II), cefuroxime (III), and ciprofloxacin (IV) to exceed a threshold PAF of 1% (A) or 0.1% (B).

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4.3.3 Comparison with measurements

Figure 4.5A shows that average surface water concentrations of pharmaceuticals only used in human medicine fall inside the 95% confidence interval of the predicted concentrations, except for the average ofloxacin concentration in a grid cell in north-eastern Spain ^[119]. López-Serna et al. ^[119] measured ofloxacin at two locations in the Llobregat river, one upstream and one downstream from the discharge point of a large STP. These locations might be considered not representative for the average concentration in the whole 100 * 100 km² grid cell, causing discrepancy between measured and predicted concentrations.

Pharmaceuticals used in both human and veterinary medicine are generally measured at higher concentrations than predicted (Fig. 4.5B). Veterinary emissions probably also explain the occurrence of chlortetracycline, oxytetracycline, tetracycline and (to a lesser extent) trimethoprim in surface waters where zero concentrations were predicted.

FIGURE 4.2

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FIGURE 4.3

Spatially varying parameters that explain at least 5% of the spatial variability of PAF_P50 values throughout Europe, for (A) oxytetracyline (B) erythromycin, (C) cefuroxime, and (D) ciprofloxacin.

Tertiary sewage treatment is considered as the fraction of the STP influents in a grid cell that receives the specified treatment. STP: sewage treatment plant; Outpatient consumption: consumption in ambulatory setting; Inpatient consumption: consumption within healthcare facilities.

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FIGURE 4.4

Importance of uncertain parameters for the estimation of PAF in grid cells where the upper confidence limit (PAF_P95) exceeds 1%. Only input parameters are displayed that explain more than 1% of the uncertainty in PAFs in at least one of these grid cells. A: oxytetracycline (34 grid cells); B: erythromycin (18 grid cells); C: cefuroxime (124 grid cells); D: ciprofloxacin (3 grid cells).

Bars represent the average importance; error bars represent minimum and maximum values. SSD: species sensitivity distribution; µ_logEC50: mean over the log-transformed EC₅₀ values; τ_logEC50: interspecies variation in

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FIGURE 4.5

Comparison of predicted and average measured surface water concentrations of pharmaceuticals only used in human medicine (A) or used in both human and veterinary medicine (B).

Coloured lines represent range between 2.5^th percentile, median and 97.5^th percentile of predicted concentra-tions, Crosses represent measurements where zero concentrations are predicted. Black line represents the 1:1 line and dotted lines represent the 1:10 and 10:1 lines.

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4.4 DISCUSSION

The added value of the incorporation of spatial information in the prioritisation of human APIs for local water management was assessed in the light of uncertainty in the input parameters of the prioritisation framework. In this section, methodological limitations are discussed, followed by an indication of the implications for research and policy.

4.4.1 Methodological limitations

Uncertainty distributions were assigned to substance-specific and country-specific input parameters, but not to grid cell-specific parameters. This decision was based on the assumptions that underlie multimedia fate modelling, i.e., steady state concentrations and a spatially homogeneous distribution of environmental media. Moreover, region-to-region variability has been previously shown to be relatively small compared with inter-chemical variability in multimedia fate modelling ^[206]. Variance in the environmental fate of chemicals, assessed via multimedia fate calculations, is therefore typically driven by uncertainty in their chemical properties, rather than by uncertainties in environmental characteristics ^[207].

This is supported by the results of the parameter importance analysis on spatially varying parameters (Fig. 4.3), which show that emission-related parameters, rather than environmental characteristics, influence the spatial variation in the environmental impact of the nine APIs. However, these results partly depend on the spatial scale considered (i.e., 100 * 100 km² grid cells). Because of the assumptions underlying the multimedia fate modelling, spatial variability is ignored within each grid cell. Instead, spatial variability is only considered as variability between environmental grid cells. Consequently, between-cell variability in concentrations becomes larger at smaller cell size, related to the increased variability in environmental characteristics ^[208]. Klepper and den Hollander

[209] showed that multimedia fate concentration predictions at a European scale might

underestimate the average surface water concentrations resulting from a spatially explicit approach with smaller grid cells, i.e., 25 * 25 km². The spatial variability between these grid cells was substantial (with maximum values approximately a factor of 1000 above the European box model average). These studies show that cell size is an important factor in the methodology presented, and that the prioritisation of APIs may change at a grid cells