In silico ecotoxicity assessment of pharmaceutical residues in wastewater following oxidative treatment.

Advanced oxidation processes such as thermal plasma activation and UV-C/H2O2 treatment are considered as applications for the degradation of pharmaceutical residues in wastewater complementary to conventional wastewater treatment. It is supposed that direct oxidative treatment can lower the toxicity of hospital sewage water (HSW). The aim of this study was to predict the ecotoxicity for three aquatic species before and after oxidative treatment of 10 quantified pharmaceuticals in hospital sewage water. With the application of oxidative chemistry, pharmaceuticals are degraded into transformation products before reaching complete mineralization. To estimate the potential ecotoxicity for fish, Daphnia and green algae ECOSAR quantitative structure-activity relationship software was used. Structure information from pristine pharmaceuticals and their oxidative transformation products were calculated separately and in a mixture computed to determine the risk quotient (RQ). Calculated mixture toxicities for 10 compounds found in untreated HSW resulted in moderate-high RQ predictions for all three aquatic species. Compared to untreated HSW, 30-min treatment with thermal plasma activation or UV-C/H2O2 resulted in lowered RQs. For the expected transformation products originating from fluoxetine, cyclophosphamide and acetaminophen increased RQs were predicted. Prolongation of thermal plasma oxidation up to 120 min predicted low-moderate toxicity in all target species. It is anticipated that further degradation of oxidative transformation products will end in less toxic aliphatic and carboxylic acid products. Predicted RQs after UV-C/H2O2 treatment turned out to be still moderate-high. In conclusion, in silico extrapolation of experimental findings can provide useful predicted estimates of mixture toxicity. However due to the complex composition of wastewater this in silico approach is a first step to screen for ecotoxicity. It is recommendable to confirm these predictions with ecotoxic bioassays.

Mapping domestic water use to quantify water-demand and water-related contaminant exposure in a peri-urban community, Indonesia.

Water use of domestic activities was quantified by interviewing 217 people in a peri-urban community near Bandung, Indonesia. Resulting in data on domestic water demand and data needed for exposure modelling of domestic activities: drinking, cooking, brushing teeth, swimming, bathing, laundry, dishwashing, religious cleansing, washing hands and cleaning food. Average total domestic water usage was 117 l/person/day, topping the WHO guidelines for basic needs (50-100 l/person/day). This water use level is comparable with higher income countries for the same set of activities but 100% higher than water use in an Indonesian traditional rural community. The final dataset provides insight in quantity of water used for domestic activities, as well as the use-frequency, duration and water sources used. These data are scarce for Indonesia and other low-middle income countries but necessary for water demand studies and estimating risks through exposure to pathogens and emerging contaminants in human exposure modelling.

A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals.

An increasing number of pharmaceuticals found in the environment potentially impose adverse effects on organisms such as fish. Physiologically based kinetic (PBK) models are essential risk assessment tools, allowing a mechanistic approach to understanding chemical effects within organisms. However, fish PBK models have been restricted to a few species, limiting the overall applicability given the countless species. Moreover, many pharmaceuticals are ionizable, and fish PBK models accounting for ionization are rare. Here, we developed a generalized PBK model, estimating required parameters as functions of fish and chemical properties. We assessed the model performance for five pharmaceuticals (covering neutral and ionic structures). With biotransformation half-lives (HLs) from EPI Suite, 73 and 41% of the time-course estimations were within a 10-fold and a 3-fold difference from measurements, respectively. The performance improved using experimental biotransformation HLs (87 and 59%, respectively). Estimations for ionizable substances were more accurate than any of the existing species-specific PBK models. The present study is the first to develop a generalized fish PBK model focusing on mechanism-based parameterization and explicitly accounting for ionization. Our generalized model facilitates its application across chemicals and species, improving efficiency for environmental risk assessment and supporting an animal-free toxicity testing paradigm.

Ibuprofen exposure in Europe; ePiE as an alternative to costly environmental monitoring.

The EU Water Framework Directive and Priority Substance Directive provide a framework to identify substances that potentially pose a risk to surface waters and provide a legal basis whereby member states are required to monitor and comply with environmental quality standards (EQSs) set for those substances. The cost and effort to continuously measure and analyse real world concentrations in all water bodies across Europe are high. Establishing the reliability of environmental exposure models to predict concentrations of priority substances is key, both to fill data gaps left by monitoring campaigns, and to predict the outcomes of actions that might be taken to reduce exposure. In this study, we aimed to validate the ePiE model for the pharmaceutical ibuprofen by comparing predictions made using the best possible consumption data with measured river concentrations. The results demonstrate that the ePiE model makes useful, conservative exposure predictions for ibuprofen, typically within a factor of 3 of mean measured values. This exercise was performed across a number of basins within Europe, representative of varying conditions, including consumption rates, population densities and climates. Incorporating specific information pertaining to the basin or country being assessed, such as custom WWTP removal rates, was found to improve the realism and accuracy of predictions. We found that the extrapolation of consumption data between countries should be kept to a minimum when modelling the exposure of pharmaceuticals, with the per capita consumption of ibuprofen varying by nearly a factor of 10.

Internal and Maternal Distribution of Persistent Organic Pollutants in Sea Turtle Tissues: A Meta-Analysis.

We aimed to identify patterns in the internal distribution of persistent organic pollutants (POPs) and assess contributing factors using sea turtles and their offspring as a case study of a long-lived wildlife species. We systematically synthesized 40 years of data and developed a lipid database to test whether lipid-normalized POP concentrations are equal among tissues as expected under steady state for lipophilic compounds. Results supported equal partitioning among tissues with high blood flow or perfusion including the heart, kidney, muscle, and lung. Observed differences in the brain, fat, and blood plasma, however, suggest the physiological influence of the blood-brain barrier, limited perfusion, and protein content, respectively. Polybrominated diphenyl ethers partitioned comparably to legacy POPs. Polycyclic aromatic hydrocarbons, meanwhile, partitioned more into the lung, colon, and muscle compared to the liver under chronic and acute field exposure. Partitioning ratios of individual POPs among tissues were significantly related to the lipophilicity of compounds (as estimated by Kow) in half of the observed cases, and significant differences between juveniles and adults underscore physiological differences across life stages. The comprehensive tissue partitioning patterns presented here provide a quantitative basis to support comparative assessments of POP pollution derived from biomonitoring among multiple tissues.

Do initial concentration and activated sludge seasonality affect pharmaceutical biotransformation rate constants?

Pharmaceuticals find their way to the aquatic environment via wastewater treatment plants (WWTPs). Biotransformation plays an important role in mitigating environmental risks; however, a mechanistic understanding of involved processes is limited. The aim of this study was to evaluate potential relationships between first-order biotransformation rate constants (kb) of nine pharmaceuticals and initial concentration of the selected compounds, and sampling season of the used activated sludge inocula. Four-day bottle experiments were performed with activated sludge from WWTP Groesbeek (The Netherlands) of two different seasons, summer and winter, spiked with two environmentally relevant concentrations (3 and 30 nM) of pharmaceuticals. Concentrations of the compounds were measured by LC-MS/MS, microbial community composition was assessed by 16S rRNA gene amplicon sequencing, and kb values were calculated. The biodegradable pharmaceuticals were acetaminophen, metformin, metoprolol, terbutaline, and phenazone (ranked from high to low biotransformation rates). Carbamazepine, diatrizoic acid, diclofenac, and fluoxetine were not converted. Summer and winter inocula did not show significant differences in microbial community composition, but resulted in a slightly different kb for some pharmaceuticals. Likely microbial activity was responsible instead of community composition. In the same inoculum, different kb values were measured, depending on initial concentration. In general, biodegradable compounds had a higher kb when the initial concentration was higher. This demonstrates that Michealis-Menten kinetic theory has shortcomings for some pharmaceuticals at low, environmentally relevant concentrations and that the pharmaceutical concentration should be taken into account when measuring the kb in order to reliably predict the fate of pharmaceuticals in the WWTP. KEY POINTS: • Biotransformation and sorption of pharmaceuticals were assessed in activated sludge. • Higher initial concentrations resulted in higher biotransformation rate constants for biodegradable pharmaceuticals. • Summer and winter inocula produced slightly different biotransformation rate constants although microbial community composition did not significantly change.

Bioconcentration of Organotin Cations during Molting Inhibits Heterocypris incongruens Growth.

The densely populated North Sea region encompasses catchments of rivers such as Scheldt and Meuse. Herein, agricultural, industrial, and household chemicals are emitted, transported by water, and deposited in sediments, posing ecological risks. Though sediment monitoring is often costly and time-intensive, modeling its toxicity to biota has received little attention. Due to high complexity of interacting variables that induce overall toxicity, monitoring data only sporadically validates current models. Via a range of concepts, we related bio-physicochemical constituents of sediment in Flanders to results from toxicity bioassays performed on the ostracod Heterocypris incongruens. Depending on the water body, we explain up to 90% of the variance in H. incongruens growth. Though variable across Flanders' main water bodies, organotin cations and ammonia dominate the observed toxicity according to toxic unit (TU) assessments. Approximately 10% relates to testing conditions/setups, species variabilities, incoherently documented pollutant concentrations, and/or bio-physicochemical sediment properties. We elucidated the influence of organotin cations and ammonia relative to other metal(oxides) and biocides. Surprisingly, the tributylin cation appeared ∼1000 times more toxic to H. incongruens as compared to "single-substance" bioassays for similar species. We inferred indirect mixture effects between organotin, ammonia, and phosphate. Via chemical speciation calculations, we observed strong physicochemical and biological interactions between phosphate and organotin cations. These interactions enhance bioconcentration and explain the elevated toxicity of organotin cations. Our study aids water managers and policy makers to interpret monitoring data on a mechanistic basis. As sampled sediments differ, future modeling requires more emphasis on characterizing and parametrizing the interactions between bioassay constituents. We envision that this will aid in bridging the gap between testing in the laboratory and field observations.

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.

Uncertainty and variability in human exposure limits - a chemical-specific approach for ciprofloxacin and methotrexate.

Human exposure limits (HELs) for chemicals with a toxicological threshold are traditionally derived using default assessment factors that account for variations in exposure duration, species sensitivity and individual sensitivity. The present paper elaborates a probabilistic approach for human hazard characterization and the derivation of HELs. It extends the framework for evaluating and expressing uncertainty in hazard characterization recently proposed by WHO-IPCS, i.e. by the incorporation of chemical-specific data on human variability in toxicokinetics. The incorporation of human variability in toxicodynamics was based on the variation between adverse outcome pathways (AOPs). Furthermore, sources of interindividual variability and uncertainty are propagated separately throughout the derivation process. The outcome is a two-dimensional human dose distribution that quantifies the population fraction exceeding a pre-selected critical effect level with an estimate of the associated uncertainty. This enables policy makers to set separate standards for the fraction of the population to be protected and the confidence level of the assessment. The main sources of uncertainty in the human dose distribution can be identified in order to plan new research for reducing uncertainty. Additionally, the approach enables quantification of the relative risk for specific subpopulations. The approach is demonstrated for two pharmaceuticals, i.e. the antibiotic ciprofloxacin and the antineoplastic methotrexate. For both substances, the probabilistic HEL is mainly influenced by uncertainty originating from: (1) the point of departure (PoD), (2) extrapolation from sub-acute to chronic toxicity and (3) interspecies extrapolation. However, when assessing the tails of the two-dimensional human dose distributions, i.e. the section relevant for the derivation of human exposure limits, interindividual variability in toxicodynamics also becomes important.

Hierarchical Bayesian Approach To Reduce Uncertainty in the Aquatic Effect Assessment of Realistic Chemical Mixtures.

Species in the aquatic environment differ in their toxicological sensitivity to the various chemicals they encounter. In aquatic risk assessment, this interspecies variation is often quantified via species sensitivity distributions. Because the information available for the characterization of these distributions is typically limited, optimal use of information is essential to reduce uncertainty involved in the assessment. In the present study, we show that the credibility intervals on the estimated potentially affected fraction of species after exposure to a mixture of chemicals at environmentally relevant surface water concentrations can be extremely wide if a classical approach is followed, in which each chemical in the mixture is considered in isolation. As an alternative, we propose a hierarchical Bayesian approach, in which knowledge on the toxicity of chemicals other than those assessed is incorporated. A case study with a mixture of 13 pharmaceuticals demonstrates that this hierarchical approach results in more realistic estimations of the potentially affected fraction, as a result of reduced uncertainty in species sensitivity distributions for data-poor chemicals.

Sensitive method for quantification of octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) in end-exhaled air by thermal desorption gas chromatography mass spectrometry.

Octamethylcyclotetrasiloxane (D4) and decamethylpentasiloxane (D5) are used as ingredients for personal care products (PCPs). Because of the use of these PCPs, consumers are exposed daily to D4 and D5. A sensitive analytical method was developed for analysis of D4 and D5 in end-exhaled air by thermal desorption gas chromatography mass spectrometry (TD-GC-MS), to determine the internal dose for consumer exposure assessment. Fifteen consumers provided end-exhaled air samples that were collected using Bio-VOC breath samplers and subsequently transferred to automatic thermal desorption (ATD) tubes. Prior to use, the ATD tubes were conditioned for a minimum of 4 h at 350 °C. The TD unit and auto sampler were coupled to a GC-MS using electron ionization. Calibration was performed using 0-10 ng/µL solutions of D4/D5 and (13)C-labeled D4/D5 as internal standards. The ions monitored were m/z 281 for D4, 355 for D5, 285 for (13)C-labeled D4, and 360 for (13)C-labeled D5. The addition of internal standard reduced the coefficient of variation from 30.8% to 9.5% for D4 and from 37.8% to 12.5% for D5. The limit of quantification was 2.1 ng/L end-exhaled air for D4 and 1.4 ng/L end-exhaled air for D5. With this method, cyclic siloxanes (D4 and D5) can be quantified in end-exhaled air at concentrations as low as background levels observed in the general population.

Testing the coherence between occupational exposure limits for inhalation and their biological limit values with a generalized PBPK-model: the case of 2-propanol and acetone.

The coherence between occupational exposure limits (OELs) and their corresponding biological limit values (BLVs) was evaluated for 2-propanol and acetone. A generic human PBPK model was used to predict internal concentrations after inhalation exposure at the level of the OEL. The fraction of workers with predicted internal concentrations lower than the BLV, i.e. the 'false negatives', was taken as a measure for incoherence. The impact of variability and uncertainty in input parameters was separated by means of nested Monte Carlo simulation. Depending on the exposure scenario considered, the median fraction of the population for which the limit values were incoherent ranged from 2% to 45%. Parameter importance analysis showed that body weight was the main factor contributing to interindividual variability in blood and urine concentrations and that the metabolic parameters Vmax and Km were the most important sources of uncertainty. This study demonstrates that the OELs and BLVs for 2-propanol and acetone are not fully coherent, i.e. enforcement of BLVs may result in OELs being violated. In order to assess the acceptability of this "incoherence", a maximum population fraction at risk of exceeding the OEL should be specified as well as a minimum level of certainty in predicting this fraction.

Mechanistically-based QSARs to describe metabolic constants in mammals.

Biotransformation is one of the processes which influence the bioaccumulation of chemicals. The enzymatic action of metabolism involves two processes, i.e. the binding of the substrate to the enzyme followed by a catalytic reaction, which are described by the Michaelis-Menten constant (Km) and the maximum rate (Vmax). Here, we developed Quantitative Structure-Activity Relationships (QSARs) for Log(1/Km) and LogVmax for substrates of four enzyme classes. We focused on oxidations catalysed by alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO) and cytochrome P450 (CYP) in mammals. The chemicals investigated were xenobiotics, including alcohols, aldehydes, pesticides and drugs. We applied general linear models for this purpose, employing descriptors related to partitioning, geometric characteristics, and electronic properties of the substrates, which can be interpreted mechanistically. The explained variance of the QSARs varied between 20% and 70%, and it was larger for Log(1/Km) than for LogVmax. The increase of 1/Km with compound logP and size suggests that weak interactions are important, e.g. by substrate binding via desolvation processes. The importance of electronic factors for 1/Km was described in relation to the catalytic mechanism of the enzymes. Vmax was particularly influenced by electronic properties, such as dipole moment and energy of the lowest unoccupied molecular orbital. This can be explained by the nature of the catalysis, characterised by the cleavage and formation of covalent or ionic bonds (strong interactions). The present study may be helpful to understand the underlying principles of the chemical specific activity of four important oxidising enzymes.

Prioritisation of data-poor pharmaceuticals for empirical testing and environmental risk assessment.

There are more than 3,500 active pharmaceutical ingredients (APIs) on the global market for human and veterinary use. Residues of these APIs eventually reach the aquatic environment. Although an environmental risk assessment (ERA) for marketing authorization applications of medicinal products is mandatory in the European Union since 2006, an ERA is lacking for most medicines approved prior to 2006 (legacy APIs). Since it is unfeasible to perform extensive ERA tests for all these legacy APIs, there is a need for prioritization of testing based on the limited data available. Prioritized APIs can then be further investigated to estimate their environmental risk in more detail. In this study, we prioritized more than 1,000 APIs used in Europe based on their predicted risk for aquatic freshwater ecosystems. We determined their risk by combining an exposure estimate (Measured or Predicted Environmental Concentration; MEC or PEC, respectively) with a Predicted No Effect Concentration (PNEC). We developed several procedures to combine the limited empirical data available with in silico data, resulting in multiple API rankings varying in data needs and level of conservativeness. In comparing empirical with in silico data, our analysis confirmed that the PEC estimated with the default parameters used by the European Medicines Agency often - but not always - represents a worst-case scenario. Comparing the ecotoxicological data for the three main taxonomic groups, we found that fish represents the most sensitive species group for most of the APIs in our list. We furthermore show that the use of in silico tools can result in a substantial underestimation of the ecotoxicity of APIs. After combining the different exposure and effect estimates into four risk rankings, the top-ranking APIs were further screened for availability of ecotoxicity data in data repositories. This ultimately resulted in the prioritization of 15 APIs for further ecotoxicological testing and/or exposure assessment.

Predicting the oral uptake efficiency of chemicals in mammals: combining the hydrophilic and lipophilic range.

Environmental risk assessment requires models for estimating the bioaccumulation of untested compounds. So far, bioaccumulation models have focused on lipophilic compounds, and only a few have included hydrophilic compounds. Our aim was to extend an existing bioaccumulation model to estimate the oral uptake efficiency of pollutants in mammals for compounds over a wide K(ow) range with an emphasis on hydrophilic compounds, i.e. compounds in the lower K(ow) range. Usually, most models use octanol as a single surrogate for the membrane and thus neglect the bilayer structure of the membrane. However, compounds with polar groups can have different affinities for the different membrane regions. Therefore, an existing bioaccumulation model was extended by dividing the diffusion resistance through the membrane into an outer and inner membrane resistance, where the solvents octanol and heptane were used as surrogates for these membrane regions, respectively. The model was calibrated with uptake efficiencies of environmental pollutants measured in different mammals during feeding studies combined with human oral uptake efficiencies of pharmaceuticals. The new model estimated the uptake efficiency of neutral (RMSE=14.6) and dissociating (RMSE=19.5) compounds with logK(ow) ranging from -10 to +8. The inclusion of the K(hw) improved uptake estimation for 33% of the hydrophilic compounds (logK(ow)<0) (r(2)=0.51, RMSE=22.8) compared with the model based on K(ow) only (r(2)=0.05, RMSE=34.9), while hydrophobic compounds (logK(ow)>0) were estimated equally by both model versions with RMSE=15.2 (K(ow)&K(hw)) and RMSE=15.7 (K(ow) only). The model can be used to estimate the oral uptake efficiency for both hydrophilic and hydrophobic compounds.

Quantification and characterization of macro- and mesoplastic items in the water column of the river Waal.

Although studies on plastic concentrations mainly focus on the marine environment, recently, an increasing number of studies point out environmental consequences in freshwater environments around the world. However, there still is a paucity of field data on the abundance of riverine plastic items, in particular in the water column. In this study, we provide an overview of macro- and mesoplastic concentrations, categories, ages, and origin over several years in the water column of the river Waal, in the Netherlands. The river water column was passively sampled at two selected locations using a stow net at very low and low discharges (range 537 - 1345 m3.s-1). The most dominant macro- and mesoplastic categories were 'Miscellaneous plastic waste', including "Plastic film 2.5 - 50 cm (soft)" and "Plastic film 0 - 2.5 cm (soft)" as main categories. Macro- and mesoplastic categories were found to show limited variability during several years of monitoring. The mean macroplastic concentration (± SD) ranged between 2.2 × 10-3 ± 0.001 and 7.4 × 10-3 ± 0.003 particles.m-3 for October 2020 and November 2018, respectively. In 2020, the plastic concentrations showed a sharp decrease compared to the previous years, most likely as a consequence of the COVID-19 crisis. The origin of the plastics (e.g., countries) also showed little variability during monitoring. The consistency of several characteristics of the collected plastic suggests that the same sources were responsible for the macro- and mesoplastic input into the river Waal during low discharges and over multiple years. We present the first temporal assessment of macro- and mesoplastic concentrations and composition in the water column of the river Waal. The outcome of the current study can be used to support the development of management measures by decision makers.

Tyre granulate on the loose; How much escapes the turf? A systematic literature review.

Tyre granulate used as infill for artificial turf is hailed by some as a good example of reuse, while others see it as a baleful means to dispose of discarded tyres. Because the particles are applied loosely to the surface, they will inevitably disperse into the environment. The possible environmental and health impacts of the particles are a source of societal concern. In response to this, policies to limit particle losses are being developed at the European level. To make informed decisions, data on the quantity of tyre granulate released into the environment are required. So far, however, there are no systematic reviews on or estimates of these losses. The aim of the present study was to identify the various pathways through which infill leaves a football turf and, subsequently, to estimate the quantity of infill leaving the turf by each of these pathways. Data on the pathways including the associated volumes were collected in a systematic literature review following the PRISMA method. The quality of the evidence reported in the retrieved literature was assessed using the GRADE method. The resulting pathways and corresponding quantities were captured in a mass balance. This study estimates that, without mitigation measures, approximately 950 kg/year (min. 570 kg/year, max. 2280 kg/year) of infill leaves the surface of an average artificial football turf via known pathways. Clearing snow can result in an additional loss of 830 kg/year (min. 200 kg/year, max. 2760 kg/year) of infill material. To mitigate the dispersion of infill, one could focus on snow removal, brushing and granulate picked up by players. Mitigation measures for these pathways are well-established and relatively easy to implement and maintain. Although the amount of granulate picked up from the turf by players is relatively small, the measure will promote environmental awareness among the players.

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.

Predicting and improving the microbial removal of organic micropollutants during wastewater treatment: A review.

Organic micropollutants (OMPs) consist of widely used chemicals such as pharmaceuticals and pesticides that can persist in surface and groundwaters at low concentrations (ng/L to µg/L) for a long time. The presence of OMPs in water can disrupt aquatic ecosystems and threaten the quality of drinking water sources. Wastewater treatment plants (WWTPs) rely on microorganisms to remove major nutrients from water, but their effectiveness at removing OMPs varies. Low removal efficiency might be the result of low concentrations, inherent stable chemical structures of OMPs, or suboptimal conditions in WWTPs. In this review, we discuss these factors, with special emphasis on the ongoing adaptation of microorganisms to degrade OMPs. Finally, recommendations are drawn to improve the prediction of OMP removal in WWTPs and to optimize the design of new microbial treatment strategies. OMP removal seems to be concentration-, compound-, and process-dependent, which poses a great complexity to develop accurate prediction models and effective microbial processes targeting all OMPs.

Compound lipophilicity as a descriptor to predict binding affinity (1/K(m)) in mammals.

In bioaccumulation models, biotransformation is one of the processes decreasing the concentration of chemicals in an organism. In order to be metabolized, a compound needs to bind to an enzyme. In this study, we derived relationships between binding affinity and lipophilicity, expressed as Log (1/K(m)) and Log K(ow), respectively. We focused on oxidations in mammals catalyzed by alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO), and cytochrome P450 (CYP) enzymes. For all regressions, 1/K(m) increased with compound K(ow), which can be understood from the tendency to biotransform lipophilic compounds into more polar, thus more easily excretable metabolites. Lipophilicity was relevant to the binding of most of the substrate classes of ADH, ALDH, and CYP. The resulting slopes had 95% Confidence Intervals covering the value of 0.63, typically noted in protein-water distribution (Log K(pw)) and Log K(ow) regressions. A reduced slope (0.2-0.3) was found for FMO: this may be due to a different reaction mechanism involving a nucleophilic attack. The general patterns of metabolism were mechanistically interpreted in terms of partitioning theory. Information on the overall principles determining biotransformation may be helpful in predicting metabolic rates.