Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach.

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Pharmaceuticals and Personal Care Products in the Aquatic Environment: How Can Regions at Risk be Identified in the Future?

Pharmaceuticals and personal care products (PPCPs) are an indispensable component of a healthy society. However, they are well-established environmental contaminants, and many can elicit biological disruption in exposed organisms. It is now a decade since the landmark review covering the top 20 questions on PPCPs in the environment (Boxall et al., 2012). In the present study we discuss key research priorities for the next 10 years with a focus on how regions where PPCPs pose the greatest risk to environmental and human health, either now or in the future, can be identified. Specifically, we discuss why this problem is of importance and review our current understanding of PPCPs in the aquatic environment. Foci include PPCP occurrence and what drives their environmental emission as well as our ability to both quantify and model their distribution. We highlight critical areas for future research including the involvement of citizen science for environmental monitoring and using modeling techniques to bridge the gap between research capacity and needs. Because prioritization of regions in need of environmental monitoring is needed to assess future/current risks, we also propose four criteria with which this may be achieved. By applying these criteria to available monitoring data, we narrow the focus on where monitoring efforts for PPCPs are most urgent. Specifically, we highlight 19 cities across Africa, Central America, the Caribbean, and Asia as priorities for future environmental monitoring and risk characterization and define four priority research questions for the next 10 years. Environ Toxicol Chem 2024;43:575-588. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems.

Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergize with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition, as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process-based models of chemical fate that do not typically allow for such high-throughput applications. Here, we describe a Bayesian network model that does. It incorporates a two-step univariate regression model based on a 30-day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in-stream exposures. When relating to aquatic risks, the influence of these processes is reduced and distributions of risk are dominated by effect-related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient >1) was 8%-12%. This percentage further increased to 30%-35% when a more conservative precautionary factor of 100 instead of 30 was used. Integr Environ Assess Manag 2024;20:384-400. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Characterization of urban sources of antibiotics and antibiotic-resistance genes in a Dutch sewer catchment.

A one year study was conducted in the city of Nijmegen, The Netherlands, to characterize various urban sources of antibiotics and antibiotic resistant genes (ARGs) in wastewater within a single sewer catchment. Prevalence of ermB, tet(W), sul1, sul2, intl1, and 16S rRNA gene was determined at 10 locations within the city. Sampling locations included a nursing home, a student residence, a hospital and an industrial area, among others. Wastewater concentrations of 23 antibiotics were measured using passive sampling. Additionally, excreted loads of 22 antibiotics were estimated based on ambulatory prescription and clinical usage data. Genes sul1 and intl1 were most abundant across most locations. Ciprofloxacin and amoxicillin together contributed over 92 % of the total estimated antibiotic selective pressure at all sampling points. The present study highlights the prominent role that hospitals can have in the prevalence and proliferation of ARGs in urban wastewater. Furthermore, results suggest that even short-term changes in the therapeutic regimen prescribed in hospitals may translate into shifting ARG abundance patterns in hospital wastewater. The methods applied present an opportunity to identify emission hotspots and prioritize intervention options to limit ARG spread from urban wastewater to the environment.

Genome-wide association reveals host-specific genomic traits in Escherichia coli.

BACKGROUND: Escherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood. RESULTS: We randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type. CONCLUSIONS: This study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli.

Regulatory Risk Assessment of Pharmaceuticals in the Environment: Current Practice and Future Priorities.

How can data on the occurrence of pharmaceuticals and personal care products (PPCPs) in the environment and the quality of ecosystems exposed to PPCPs be used to determine whether current regulatory risk assessment schemes are effective? This is one of 20 "big questions" concerning PPCPs in the environment posed in a landmark review paper in 2012. Ten years later, we review the developments around this question, focusing on the first P in PPCPs, that is, pharmaceuticals, or more specifically the active ingredients included in them (active pharmaceutical ingredients, APIs). We illustrate how extensive data on both the occurrence of APIs and the ecotoxicological sensitivity of aquatic species to them can be used in a retrospective risk assessment. In the Netherlands, current regulatory risk assessment schemes offer insufficient protection against direct ecotoxicological effects from APIs: the toxic pressure exerted by the 39 APIs included in our study exceeds the policy-related protective threshold of 0.05 (the "95%-protection level") in at least 13% of sampled surface waters. In general, anti-inflammatory and antirheumatic products (e.g., diclofenac, ibuprofen) contributed most to the overall toxic pressure, followed by sex hormones and modulators of the genital system (e.g., ethinylestradiol) and psychoanaleptics (e.g., caffeine). We formulated three open questions for future research. The first relates to improving the availability and accessibility of good-quality ecotoxicity data on pharmaceuticals for the global scientific, regulatory, and general public. The second relates to the adaptation of regulatory risk assessment frameworks for developing regions of the world. The third relates to the integration of effect-based and ecological approaches into regulatory risk assessment practice. Environ Toxicol Chem 2023;00:1-12. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Human health risk assessment of pharmaceuticals in the European Vecht River.

Active pharmaceutical ingredients (APIs) can reach surface waters used for drinking water extraction and recreational activities, such as swimming and fishing. The aim of the present study was to systematically assess the lifetime human health risks posed by 15 individual APIs and their mixtures occurring in the German-Dutch transboundary Vecht River. An exposure model was developed and used to assess the combined risks of oral and dermal exposure under a variety of exposure conditions. A total of 4500 API uptake values and 165 lifetime risk values were estimated for 15 and 11 APIs, respectively. Overall, the lifetime human health risks posed by the APIs and their mixtures based on modeling results were deemed acceptable under typical exposure conditions. Under very extreme environmental conditions and human behavior, API mixture risks were of potential concern while the risks of individual APIs were negligible, with a few exceptions. The antibiotic doxycycline and analgesic phenazone showed the highest and lowest risks, respectively. The study did not evaluate the potential risks caused by metabolite compounds. Recommendations for water managers are provided to help improve the accuracy and utility of human health risk assessments of pharmaceuticals. Integr Environ Assess Manag 2022;18:1639-1654. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Ecological Risk Assessment of Pharmaceuticals in the Transboundary Vecht River (Germany and The Netherlands).

Millions of people rely on active pharmaceutical ingredients (APIs) to prevent and cure a wide variety of illnesses in humans and animals, which has led to a steadily increasing consumption of APIs across the globe and concurrent releases of APIs into the environment. In the environment, APIs can have a detrimental impact on wildlife, particularly aquatic wildlife. Therefore, it is essential to assess their potential adverse effects to aquatic ecosystems. The European Water Framework Directive sets out that risk assessment should be performed at the catchment level, crossing borders where needed. The present study defines ecological risk profiles for surface water concentrations of 8 APIs (carbamazepine, ciprofloxacin, cyclophosphamide, diclofenac, erythromycin, 17α-ethinylestradiol, metformin, and metoprolol) in the Vecht River, a transboundary river that crosses several German and Dutch regions. Ultimately, 3 main goals were achieved: 1) the geo-referenced estimation of API concentrations in surface water using the geography-referenced regional exposure assessment tool for European rivers; 2) the derivation of new predicted-no-effect concentrations for 7 of the studied APIs, of which 3 were lower than previously derived values; and 3) the creation of detailed spatially explicit ecological risk profiles of APIs under 2 distinct water flow scenarios. Under average flow conditions, carbamazepine, diclofenac, and 17α-ethinylestradiol were systematically estimated to surpass safe ecological concentration thresholds in at least 68% of the catchment's water volume. This increases to 98% under dry summer conditions. Environ Toxicol Chem 2022;41:648-662. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The importance of over-the-counter-sales and product format in the environmental exposure assessment of active pharmaceutical ingredients.

When assessing the environmental exposure of active pharmaceutical ingredients (APIs), the mass contributed from over the counter (OTC) sales are often not included due to difficulty obtaining this data and topical formats are overlooked completely. This study presents a comprehensive approach, investigating the significance of OTC and topical applications as sources of API releases to wastewater, in addition to temporal and subnational variations in use in the UK. The study provides methods to obtain and make use of OTC sales data which can be applied widely. The calculated releases to wastewater compared well with influent concentrations measured at several UK wastewater treatment plants (WWTPs). Consistent overestimation was observed, attributed to a number of factors, including in-sewer removal. OTC sales were found to make up a large proportion of the mass of ibuprofen (76%) and diclofenac (35%) consumed and topical formats were also found to be vital, contributing disproportionately to wastewater loadings per unit mass of ibuprofen and diclofenac used (43% and 99% of the total mass released, respectively). Releases of the APIs investigated did not vary temporally, but regional variation was significant and where possible should be considered for the most accurate exposure assessment of pharmaceuticals.

Filling the gaps in the global prevalence map of clinical antimicrobial resistance.

Surveillance is critical in containing globally increasing antimicrobial resistance (AMR). Affordable methodologies to prioritize AMR surveillance efforts are urgently needed, especially in low- and middle-income countries (LMICs), where resources are limited. While socioeconomic characteristics correlate with clinical AMR prevalence, this correlation has not yet been used to estimate AMR prevalence in countries lacking surveillance. We captured the statistical relationship between AMR prevalence and socioeconomic characteristics in a suite of beta-binomial principal component regression models for nine pathogens resistant to 19 (classes of) antibiotics. Prevalence data from ResistanceMap were combined with socioeconomic profiles constructed from 5,595 World Bank indicators. Cross-validated models were used to estimate clinical AMR prevalence and temporal trends for countries lacking data. Our approach provides robust estimates of clinical AMR prevalence in LMICs for most priority pathogens (cross-validated q2 > 0.78 for six out of nine pathogens). By supplementing surveillance data, 87% of all countries worldwide, which represent 99% of the global population, are now informed. Depending on priority pathogen, our estimates benefit 2.1 to 4.9 billion people living in countries with currently insufficient diagnostic capacity. By estimating AMR prevalence worldwide, our approach allows for a data-driven prioritization of surveillance efforts. For carbapenem-resistant Acinetobacter baumannii and third-generation cephalosporin-resistant Escherichia coli, specific countries of interest are located in the Middle East, based on the magnitude of estimates; sub-Saharan Africa, based on the relative prevalence increase over 1998 to 2017; and the Pacific Islands, based on improving overall model coverage and performance.

Mean Species Abundance as a Measure of Ecotoxicological Risk.

Chemical pollution of surface waters is considered an important driver for recent declines in biodiversity. Species sensitivity distributions (SSDs) are commonly used to evaluate the ecological risks of chemical exposure, accounting for variation in interspecies sensitivity. However, SSDs do not reflect the effects of chemical exposure on species abundance, considered an important endpoint in biological conservation. Although complex population modeling approaches lack practical applicability when it comes to the routine practice of lower tier chemical risk assessment, in the present study we show how information from widely available laboratory toxicity tests can be used to derive the change in mean species abundance (MSA) as a function of chemical exposure. These exposure-response MSA relationships combine insights into intraspecies exposure-response relationships and population growth theory. We showcase the practical applicability of our method for cadmium, copper, and zinc, and include a quantification of the associated statistical uncertainty. For all 3 metals, we found that concentrations hazardous for 5% of the species (HC5 s) based on MSA relationships are systematically higher than SSD-based HC5 values. Our proposed framework can be useful to derive abundance-based ecological protective criteria for chemical exposure, and creates the opportunity to assess abundance impacts of chemical exposure in the context of various other anthropogenic stressors. Environ Toxicol Chem 2020;39:2304-2313. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Reliable and representative in silico predictions of freshwater ecotoxicological hazardous concentrations.

A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>105) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 1012. We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment.

Risk-management tool for environmental prioritization of pharmaceuticals based on emissions from hospitals.

Over the past decade, the health care sector has become increasingly aware of the impact of pharmaceutical emissions to the environment. Yet, it remains unclear which compounds are the most relevant to address and at what point emission control is most effective. This study presents a modelling framework to prioritize pharmaceuticals based on their relative risks for aquatic organisms, using purchase and prescription data from hospitals. The framework consists of an emission prediction module and a risk prioritization module. The emission prediction module accounts for three different routes of intake (oral, intravenous, rectal), for non-patient consumption, and for delayed athome excretion due to relatively long half-lives or prescription durations of selected pharmaceuticals. We showcase the modelling framework with 16 pharmaceuticals administered at two Dutch academic hospitals. Predictions were validated with experimental data from passive sampling in the sewer system. With the exception of metformin, all predictions were within a factor of 10 from measurements. The risk prioritization module ranks each pharmaceutical based on its predicted relative risk for aquatic organisms. The resulting prioritization suggests that emission mitigation strategies should mainly focus on antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs).

QSAR-Based Estimation of Species Sensitivity Distribution Parameters: An Exploratory Investigation.

Ecological risk assessments are hampered by limited availability of ecotoxicity data. The present study aimed to explore the possibility of deriving species sensitivity distribution (SSD) parameters for nontested compounds, based on simple physicochemical characteristics, known SSDs for data-rich compounds, and a quantitative structure-activity relationship (QSAR)-type approach. The median toxicity of a data-poor chemical for species assemblages significantly varies with values of the physicochemical descriptors, especially when based on high-quality SSD data (from either acute median effect concentrations or chronic no-observed-effect concentrations). Beyond exploratory uses, we discuss how the precision of QSAR-based SSDs can be improved to construct models that accurately predict the SSD parameters of data-poor chemicals. The current models show that the concept of QSAR-based SSDs supports screening-level evaluations of the potential ecotoxicity of compounds for which data are lacking. Environ Toxicol Chem 2019;38:2764-2770. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Environmental Risk Assessment for the Active Pharmaceutical Ingredient Mycophenolic Acid in European Surface Waters.

An environmental risk assessment is presented for mycophenolic acid (MPA), an immunosuppressive pharmaceutical used for prevention of organ rejection, and its prodrug mycophenolate mofetil (MPM). Mycophenolic acid will not significantly adsorb to activated sludge. In activated sludge, 14 C-MPA attained >80% degradation, supporting an older environmental fate test with the same compound. Based on n-octanol/water distribution coefficient (log DOW ) values of 2.28, 0.48, and ≤-1.54 at pH 5, 7, and 9, respectively, MPA is not expected to bioaccumulate. Sales amounts of MPA+MPM in Europe were used to derive predicted environmental concentrations (PECs) in surface waters; PECs were refined by including expected biodegradation in sewage treatment, average drinking water use, and average dilution of the effluents in the receiving waters per country. In addition, the exposure to pharmaceuticals in the environment (ePiE) model was run for 4 European catchments. The PECs were complemented with 110 measured environmental concentrations (MECs), ranging from below the limit of quantitation (<0.001 µg/L) to 0.656 µg/L. Predicted no-effect concentrations (PNECs) were derived from chronic tests with cyanobacteria, green algae, daphnids, and fish. The comparison of PECs and MECs with the PNECs resulted in a differentiated environmental risk assessment in which the risk ratio of PEC/PNEC or MEC/PNEC was <1 in most cases (mostly >90%), meaning no significant risk, but a potential risk to aquatic organisms in generally <10% of instances. Because this assessment reveals a partial risk, the following questions must be asked: How much risk is acceptable? and Through which measures can this risk be reduced? These questions are all the more important in view of limited alternatives for MPM and MPA and the serious consequences of not using them. Environ Toxicol Chem 2019;38:2259-2278. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Confronting variability with uncertainty in the ecotoxicological impact assessment of down-the-drain products.

The use of down-the-drain products and the resultant release of chemicals may lead to pressures on the freshwater environment. Ecotoxicological impact assessment is a commonly used approach to assess chemical products but is still influenced by several uncertainty and variability sources. As a result, the robustness and reliability of such assessments can be questioned. A comprehensive and systematic assessment of these sources is, therefore, needed to increase their utility and credibility. In this study, we present a method to systematically analyse the uncertainty and variability of the potential ecotoxicological impact (PEI) of chemicals using a portfolio of 54 shampoo products. We separately quantified the influence of the statistical uncertainty in the prediction of physicochemical properties and freshwater toxicity as predicted from Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) respectively, and of various sources of spatial and technological variability as well as variability in consumer habits via 2D Monte Carlo simulations. Overall, the variation in the PEIs of shampoo use was mainly the result of uncertainty due to the use of toxicity data from three species only. All uncertainty sources combined resulted in PEIs ranging on average over seven orders of magnitude (ratio of the 90th to the 10th percentile) so that an absolute quantification of the ecological risk would not be meaningful. In comparison, variation in shampoo composition was the most influential source of variability, although less than compared to uncertainty, leading to PEIs ranging over three orders of magnitude. Increasing the number of toxicity data by increasing the number of species, either through additional measurements or ecotoxicological modelling (e.g. using Interspecies Correlation Equations), should get priority to improve the reliability of PEIs. These conclusions are not limited to shampoos but are applicable more generally to the down-the-drain products since they all have similar data limitations and associated uncertainties relating to the availability of ecotoxicity data and variability in consumer habits and use.

Modelling environmental antibiotic-resistance gene abundance: A meta-analysis.

The successful treatment of infectious diseases heavily relies on the therapeutic usage of antibiotics. However, the high use of antibiotics in humans and animals leads to increasing pressure on bacterial populations in favour of resistant phenotypes. Antibiotics reach the environment from a variety of emission sources and are being detected at relatively low concentrations. Given the possibility of selective pressure to occur at sub-inhibitory concentrations, the ecological impact of environmental antibiotic levels on microbial communities and resistance levels is vastly unknown. Quantification of antibiotic-resistance genes (ARG) and of antibiotic concentrations is becoming commonplace. Yet, these two parameters are often assessed separately and in a specific spatiotemporal context, thus missing the opportunity to investigate how antibiotics and ARGs relate. Furthermore, antibiotic (multi)resistance has been receiving ever growing attention from researchers, policy-makers, businesses and civil society. Our aim was to collect the limited data on antibiotic concentrations and ARG abundance currently available to explore if a relationship could be defined in surface waters, sediments and wastewaters. A metric of antibiotic selective pressure, i.e. the sum of concentrations corrected for microbial inhibition potency, was used to correlate the presence of antibiotics in the environment to total relative abundance of ARG while controlling for basic sources of non-independent variability, such as country, year, study, sample and antibiotic class. The results of this meta-analysis show a significant statistical effect of antibiotic pressure and type of environmental compartment on the increase of ARG abundance even at very low levels. If global environmental antibiotic pollution continues, ARG abundance is expected to continue as well. Moreover, our analysis emphasizes the importance of integrating existing information particularly when attempting to describe complex relationships with limited mechanistic understanding.

A High-Resolution Spatial Model to Predict Exposure to Pharmaceuticals in European Surface Waters: ePiE.

Environmental risk assessment of pharmaceuticals requires the determination of their environmental exposure concentrations. Existing exposure modeling approaches are often computationally demanding, require extensive data collection and processing efforts, have a limited spatial resolution, and have undergone limited evaluation against monitoring data. Here, we present ePiE (exposure to Pharmaceuticals in the Environment), a spatially explicit model calculating concentrations of active pharmaceutical ingredients (APIs) in surface waters across Europe at ∼1 km resolution. ePiE strikes a balance between generating data on exposure at high spatial resolution while having limited computational and data requirements. Comparison of model predictions with measured concentrations of a diverse set of 35 APIs in the river Ouse (UK) and Rhine basins (North West Europe), showed around 95% were within an order of magnitude. Improved predictions were obtained for the river Ouse basin (95% within a factor of 6; 55% within a factor of 2), where reliable consumption data were available and the monitoring study design was coherent with the model outputs. Application of ePiE in a prioritisation exercise for the Ouse basin identified metformin, gabapentin, and acetaminophen as priority when based on predicted exposure concentrations. After incorporation of toxic potency, this changed to desvenlafaxine, loratadine, and hydrocodone.

Environmental impact of switching from the synthetic glucocorticoid prednisolone to the natural alkaloid berberine.

Low amounts of human pharmaceuticals in the aquatic environment can affect bacteria, animals and ultimately humans. Here, the environmental consequences of a shift in prescription behavior from prednisolone to berberine was modeled using an environmental decision support system based on four consecutive steps: emission, fate, exposure and effect. This model estimates the relative aquatic and human health impacts of alternative pharmaceutical prescriptions throughout Europe. Since a Defined Daily Dose (DDD) of berberine has yet to be formulated, the environmental impacts of berberine and prednisolone were compared under the assumption of equal DDDs. Subsequently, the relative impact ratio indicates the extent to which the actual DDD of berberine might be higher to still be environmentally preferable over prednisolone. In fact, berberine can be administered at a six times higher dose throughout Europe before its impact on the aquatic environment exceeds that of one prescription of prednisolone. On average, the results for impacts on human health are similar, with the median impact ratio ranging between 5.87 and 22.8 depending on the level of drinking water purification. However, for some regions in Spain, Austria, Baltic States and Finland, berberine can only be considered an environmentally better alternative if it is administered at a lower dose than prednisolone. We conclude that for most regions in Europe it is, up until a certain dose of berberine, beneficial for the aquatic environment and therefore human health to prefer prescription of berberine over prednisolone.