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Incidence of autoimmune disorders, birth defects, and neurological diseases rose over the past 50 years due to increasing variety and quantity of pollutants. To date, there appear few methods capable to evaluate and predict mixture effects by endocrine disruptors (EDs). For the first time, we have developed calculus to determine mixture effects by all kinds of EDs. Our method uses the golden ratio ϕ and draws from bifurcation and chaos theory. Using also the concept of molecular mimicry, we developed the equation: e f f e c t = 100 % 1 + e 5 · ∑ K i C i - n i ϕ 3 . We successfully tested the equation using a range of cohort studies and biomarkers, and for different pollutants like heavy metals, thyroid hormone mimickants, chromate/chlorate, etc. The equation is simple enough to use with only minor prior knowledge and understanding of basic algebra. The method is universal and calculation is data 'light', requiring only pollutant concentrations [ C ], potencies K and an integer n for endocrinal involvement. This study offers a comprehensive framework to assess the health effects of pollutant exposure across diverse populations, envisioning far-reaching impact, and presenting practical examples and insights., Competing Interests: The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author.)

Current challenges in (eco)toxicology are in understanding the transformation of (reactive) substances, and how transformation affects toxic modes of action. Empirical assessment of transformation products of, practically an infinite number of substances, via experimentation, is impossible. Predicting transformation products for (benchmarking) compounds from conditions, facilitates risk analyses. This study applied calculus to predict transformation products of an important environmental and medicinal/toxicological marker, carbamazepine. As radicals are ubiquitous in humans and the environment, we looked into radical-mediated transformations of carbamazepine as a benchmark. We calculated proportions of their speciation states as function of redox conditions, which we took as pH and O 2 concentration, describing transformation via covalent and ionic interactions. Formation of ring-contracted products with neuro-immunological activity is thermodynamically favored under anaerobic conditions and at low pH. Experimentally observed product distributions and toxicities reflect that pattern. Our predictive method may support toxicity predictions for other substances and conditions 'similar' to the current case study via interpolation. This paves the way for a more coherent, effective and easier risk assessment of transformation products., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Petroleum refinery effluents (PRE) are wastewaters from industries associated with oil refining. Within Europe, PREs are regulated through local discharge permits and receive substantial treatment before emission. After treatment, PREs can still contain low levels of various pollutants potentially toxic to organisms. Earlier work, including whole-effluent toxicity assessments, has shown that the toxicity of permitted PREs is often limited. However, the extent to which PREs contribute to chemical pollution already present in the receiving environment is unknown. Therefore, our study aimed to assess the contribution of PREs to mixture toxic pressure in the environment, using the multi-substance potentially affected fraction of species (msPAF) as an indicator. Based on measured chemical concentrations, compiled species sensitivity distributions (SSD) and a mechanistic solubility model, msPAF levels were estimated for undiluted effluents at discharge points and diluted effluents downstream in receiving waters. Median msPAF-chronic and msPAF-acute levels of PREs at discharge points were 74% (P50) and 40% (P95), respectively. The calculated msPAF levels were reduced substantially to <5% downstream for most effluents (82%), indicating low to negligible toxicity of PREs in receiving environments beyond the initial mixing zone. Regardless of differences in endpoints and locations, hydrocarbons (mainly total petroleum hydrocarbons) and inorganics (mainly ammonia) explained at least 85% of the mixture toxic pressure. The msPAF levels of PREs were on average 2.5-4.5 orders of magnitude lower than msPAF levels derived from background pollution levels, suggesting that PREs were minor contributors to the toxic pressure in the environment. This study presents a generic methodology for quantifying the potential toxic pressure of PREs in the environment, identifying hotspots where more effective wastewater treatment could be needed. We explicitly discuss the uncertainties for further refinement and development of the method., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Markus Hjort and Yves Verhaegen works for Concawe, which represents the European fuel manufacturing industry, whose members operate the refinery sites included in the study. Concawe is a non-profit scientific organisation established in 1963 to conduct research on environmental issues relevant to the European fuel manufacturing industry. Concawe's operating principles aim to publish all research publicly, either on the Concawe website (https://www.concawe.eu) or as open access papers in scientific journals. Concawe has a long history of publications. Our mechanism to avoid bias is to publish our work, publish our data and methods so that the research community can reproduce and build on our findings. Mathijs G.D. Smit and Aaron D. Redman works for Shell and ExxonMobil, respectively, which are Concawe member companies. As this study was funded by Concawe these co-authors are bound to follow the mission and operating principles of Concawe. Jiaqi Wang, Tom M. Nolte, A. Jan Hendriks are employed by Radboud University and have received research funding from Concawe for conducting this study. Their research group independently carries out studies financed by university, governmental, commercial and other funding that is published in peer review journals., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Nanotechnology brings benefits in fields such as biomedicine but nanoparticles (NPs) may also have adverse health effects. The effects of surface-modified NPs at the cellular level have major implications for both medicine and toxicology. Semi-empirical and mechanism-based models aid to understand the cellular transport of various NPs and its implications for quantitatively biological exposure while avoiding large-scale experiments. We hypothesized relationships between NPs-cellular elimination, surface functionality and elimination pathways by cells. Surface free energy components were used to characterize the transport of NPs onto membranes and with lipid vesicles, covering both influences by size and hydrophobicity of NPs. The model was built based on properties of neutral NPs and cells, defining Van de Waals forces, electrostatic forces and Lewis acid-base (polar) interactions between NPs and vesicles as well as between vesicles and cell membranes. We yielded a generic model for estimating exocytosis rate constants of various neutral NPs by cells based on the vesicle-transported exocytosis pathways. Our results indicate that most models are well fitted (R 2 ranging from 0.61 to 0.98) and may provide good predictions of exocytosis rate constants for NPs with differing surface functionalities (prediction errors are within 2 times for macrophages). Exocytosis rates differ between cancerous cells with metastatic potential and non-cancerous cells. Our model provides a reference for cellular elimination of NPs, and intends for medical applications and risk assessment., (© 2022. The Author(s).)

Preserving human and environmental health requires anthropogenic pollutants to be biologically degradable. Depending on concentration, both nutrients and pollutants induce and activate metabolic capacity in the endemic bacterial consortium, which in turn aids their degradation. Knowledge on such 'acclimation' is rarely implemented in risk assessment cost-effectively. As a result, an accurate description of the mechanisms and kinetics of biodegradation remains problematic. In this study, we defined a yield 'effectivity', comprising the effectiveness at which a pollutant (substrate) enhances its own degradation by inducing (biomass) cofactors involved therein. Our architecture for calculation represents the interplay between concentration and metabolism via both stoichiometric and thermodynamic concepts. The calculus for yield 'effectivity' is biochemically intuitive, implicitly embeds co-metabolism and distinguishes 'endogenic' from 'exogenic' substances' reflecting various phenomena in biodegradation and bio-transformation studies. We combined data on half-lives of pollutants/nutrients in wastewater and surface water with transition-state rate theory to obtain also experimental values for effective yields. These quantify the state of acclimation: the portion of biodegradation kinetics attributable to (contributed by) 'natural metabolism', in view of similarity to natural substances. Calculated and experimental values showed statistically significant correspondence. Particularly, carbohydrate metabolism and nucleic acid metabolism appeared relevant for acclimation (R 2  = 0.11-0.42), affecting rates up to 10 4.9(±0.7) times: under steady-state acclimation, a compound stoichiometrically identical to carbohydrates or nucleic acids, is 10 3.2 to 10 4.9 times faster aerobically degraded than a compound marginally similar. Our new method, simulating (contribution by) the state of acclimation, supplements existing structure-biodegradation and kinetic models for predicting biodegradation in wastewater and surface water. The accuracy of prediction may increase when characterizing nutrients/co-metabolites in terms of, e.g., elemental analysis. We discuss strengths and limitations of our approach by comparison to empirical and mechanism-based methods., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.

Nanoparticles (NPs) are widely used in industry and technology due to their small size and versatility, which makes them easy to enter organisms and pose threats to human and ecological health. Given the particularity and complex structure of NPs, statistical models alone cannot reliably predict uptake. Hence, we developed a generic model for predicting the cellular uptake of NPs with organic coatings, based on physicochemical interactions underlying uptake. The model utilized the concentration, experimental conditions and properties of NPs viz. size, surface coating and coverage. These parameters were converted to surface energy components and surface potentials, and combined with the components and potential for a cell membrane. For NPs uptake, we constructed energetic profiles and barriers for adsorption and permeation onto/through cell membranes. The relationships derived were compared to experimental uptake data. The model provided accurate and robust uptake estimates for neutrally charged unhalogenated NPs and six different cell types. We envision that the model provides a reference for cellular accumulation of neutral NPs and (ecological/human) risk assessment of NPs or microparticles., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Antibiotic resistance in environmental matrices becomes urgently significant for public health and has been considered as an emerging environmental contaminant. In this work, the ampicillin-resistant Escherichia coli (AR E. coli) and corresponding resistance genes (bla TEM-1 ) were effectively eliminated by the electrocatalytic process, and the dissemination risk of antibiotic resistance was also investigated. All the AR E. coli (∼8 log) was inactivated and 8.17 log bla TEM-1 was degraded by the carbon nanotubes/agarose/titanium (CNTs/AG/Ti) electrode within 30 min. AR E. coli was inactivated mainly attributing to the damage of cell membrane, which was attacked by reactive oxygen species and subsequent leakage of intracellular cytoplasm. The bla TEM-1 was degraded owing to the strand breaking in the process of electrocatalytic degradation. Furthermore, the dissemination risk of antibiotic resistance was effectively controlled after being electrocatalytic treatment. This study provided an effective electrocatalytic technology for the inactivation of antibiotic resistant bacteria and control of antibiotic resistance dissemination risk in the aqueous environment., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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 (k b ) 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 k b 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 k b for some pharmaceuticals. Likely microbial activity was responsible instead of community composition. In the same inoculum, different k b values were measured, depending on initial concentration. In general, biodegradable compounds had a higher k b 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 k b 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., (© 2021. The Author(s).)

Agricultural, industrial and household chemicals are emitted in large rivers along populated areas, transported by water and deposited in sediments, posing (eco)toxicological risks. Sediments have received less attention than surface waters, likely because of the intrinsic complexity of interactions between sediment constituents complicating correct framing of exposures. Sadly, thorough assessment of the in situ behavior of sediment constituents in bioassays is often not practical. Alternatively, we related physicochemical properties of sediments from field testing to results from bioassays. The case study covers Flemish sediment (incl. Scheldt and Meuse) and mortality of Hyalella azteca, a sensitive bio-indicator. Though variable across Flanders' main water bodies, heavy metals and ammoniacal nitrogen dominate the observed toxicity according to toxic unit (TU) assessments. Depending on the water body we explain between 50 and 90% of the variance in the observed H. azteca mortality, substantially more than previous ecotoxicity studies. We attribute the remaining variance to potential incoherently documented biophysicochemical sediment properties and concentrations of non-target biocides, testing conditions/set-ups and/or species variabilities. We discuss the relative influence of heavy metals/metaloxides, nitrogen (e.g. fertilizer), polycyclic aromatics and organochlorides. We highlight both direct and indirect mortality mechanisms. We note potential synergetic mixture effects between ammoniacal nitrogen and chromium. Such synergy may be phenomenological of 'standard' aerobic bioassays, and prove a complementary method alongside the 'acid-volatile sulfide test' to more effectively link concentration to toxicity. Future study ought to include variation in biophysicochemical properties between sampling locations and batch bioassays. Our approach enables water managers to interpret their monitoring data by converting sediment concentrations to H. azteca mortality and prioritize substances that contribute most., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Chemical pollution impinges on the quality of water systems and the ecosystem services (ESs) they provide. Expression of ESs in monetary units has become an essential tool for sustainable ecosystem management. However, the impact of chemical pollution on ESs is rarely quantified, and ES valuation often focuses on individual services without considering the total services provided by the ecosystem. The purpose of the study was to develop a stepwise approach to quantify the impact of sediment pollution on the total ES value provided by water systems. Thereby, we calculated the total ES value loss as a function of the multi-substance potentially affected fraction of species at the HC50 level (msPAF(HC50)). The function is a combination of relationships between, subsequently: the msPAF(HC50), diversity, productivity and total ES value. Regardless of the inherent differences between terrestrial and aquatic ecosystems, an increase of diversity generally corresponded to an increase in productivity with curvilinear or linear effects. A positive correlation between productivity and total values of ESs of biomes was observed. The combined relationships showed that 1% msPAF(HC50) corresponded to on average 0.5% (0.05-1.40%) of total ES value loss. The ES loss due to polluted sediments in the Waal-Meuse river estuary (the Netherlands) and Flemish waterways (Belgium) was estimated to be 0.3-5 and 0.6-10 thousand 2007$/ha/yr, respectively. Our study presents a novel methodology to assess the impact of chemical exposure on diversity, productivity, and total value that ecosystems provide. With sufficient monitoring data, our generic methodology can be applied for any chemical and region of interest and help water managers make informed decisions on cost-effective measures to remedy pollution. Acknowledging that the ES loss estimates as a function of PAF(HC50) are crude, we explicitly discuss the uncertainties in each step for further development and application of the methodology., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

Chemistry describes transformation of matter with reaction equations and corresponding rate constants. However, accurate rate constants are not always easy to get. Here we focus on radical oxidation reactions. Analysis of over 500 published rate constants of hydroxyl radicals led us to hypothesize that a modified linear free-energy relationship (LFER) could be used to predict rate constants speedily, reliably and accurately. LFERs correlate the Gibbs activation-energy with the Gibbs energy of reaction. We calculated the latter as the sum of one-electron transfer and, if appropriate, proton transfer. We parametrized specific transition state effects to orbital delocalizability and the polarity of the reactant. The calculation time for 500 reactions is less than 8 hours on a standard desktop-PC. Rate constants were also calculated for hydrogen and methyl radicals; these controls show that the predictions are applicable to a broader set of oxidizing radicals. An accuracy of 30-40% (standard deviation) with reference to reported experimental values was found suitable for the screening of complex chemical systems for possibly relevant reactions. In particular, potentially relevant reactions can be singled out and scrutinized in detail when prioritizing chemicals for environmental risk assessment.

Attenuation of organic compounds in sewage treatment plants (STPs) is affected by a complex interplay between chemical (e.g. ionization, hydrolysis), physical (e.g. sorption, volatilization), and biological (e.g. biodegradation, microbial acclimation) processes. These effects should be accounted for individually, in order to develop predictive cheminformatics tools for STPs. Using measured data from 70 STPs in the Netherlands for 69 chemicals (pharmaceuticals, herbicides, etc.), we highlighted the influences of 1) chemical ionization, 2) sorption to sludge, and 3) acclimation of the microbial consortia on the primary removal of chemicals. We used semi-empirical corrections for each of these influences to deduce biodegradation rate constants upon which quantitative structure-biodegradation relationships (QSBRs) were developed. As shown by a global QSBR, biodegradation in STPs generally relates to structural complexity, size, energetics, and charge distribution. Statistics of the global QSBR were reasonable, being R 2 training =0.69 (training set of 51 compounds) and R 2 validation =0.50 (validation set of 18 compounds). Class-specific QSBRs utilized electronic properties potentially relating to rate-limiting enzymatic steps. For class-specific QSBRs, values of R 2 of in between 0.7 and 0.8 were obtained. With caution, environmental risk assessment methodologies may apply these models to estimate biodegradation rates for 'data-poor' compounds. The approach also highlights 'meta data' on STP operational parameters needed to develop QSBRs of better predictability in the future., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Both synthetic polymers (membranes, coatings, packaging) and natural polymers (DNA, proteins) are subject to radical-initiated degradation. In order to mitigate the deterioration of the polymer properties, antioxidant strategies need to be devised. We studied the reactions of poly(α-methylstyrene sulfonate), a model compound for fuel cell membrane materials, with different degrees of polymerization with OH˙ radicals as well as subsequent reactions. We observed the resulting OH˙-adducts to react with oxygen and eliminate H 2 O, the relative likelihood of which is determined by pH and molecular weight. The resulting radical cations can be reduced back to the parent molecule by cerium(iii). This 'repair' reaction is also dependent on molecular weight likely because of intramolecular stabilization. The results from this study provide a starting point for the development of new hydrocarbon-based ionomer materials for fuel cells that are more resistant to radical induced degradation through the detoxification of intermediates via damage transfer and repair pathways. Furthermore, a more fundamental understanding of the mechanisms behind conventional antioxidants in medicine, such as ceria nanoparticles, is achieved.

The reaction between superoxide (O 2 •- ) and organic chemicals is of interest in many scientific disciplines including biology and synthetic chemistry, as well as for the evaluation of chemical fate in the environment. Due to limited data and lack of congeneric modelling, the involvement of superoxide in many complex processes cannot be adequately evaluated. In this study, we developed new quantitative structure-property relationship (QSPR) models for the prediction of the aqueous-phase rate constant for the reaction between superoxide and a wide variety of organic chemicals reacting via one-electron oxidation, reduction and hydrogen-transfer. It is shown that the relative importance of these pathways is related to frontier molecular orbital (FMO) interaction and to pH. The class-specific QSPRs developed have good statistics (0.84 ≤  R 2  ≤ 0.92). For non-congeneric chemicals it is demonstrated that the reactivity toward superoxide can be described by applying explicit descriptions for competition kinetics and speciation. Therefore, the relationships developed in this study are useful as a starting point to evaluate more complex molecules having, for example, multiple reactive functional groups, labile H bonds, or delocalised cationic charges. However, additional kinetic data and more rigorous computation are needed to evaluate such molecules.

Microbial biomass and acclimation can affect the removal of organic chemicals in natural surface waters. In order to account for these effects and develop more robust models for biodegradation, we have compiled and curated removal data for un-acclimated (pristine) surface waters on which we developed quantitative structure-activity relationships (QSARs). Global analysis of the very heterogeneous dataset including neutral, anionic, cationic and zwitterionic chemicals (N = 233) using a random forest algorithm showed that useful predictions were possible (Q ext 2 = 0.4-0.5) though relatively large standard errors were associated (SDEP ∼0.7). Classification of the chemicals based on speciation state and metabolic pathway showed that biodegradation is influenced by the two, and that the dependence of biodegradation on chemical characteristics is non-linear. Class-specific QSAR analysis indicated that shape and charge distribution determine the biodegradation of neutral chemicals (R 2 ∼ 0.6), e.g. through membrane permeation or binding to P450 enzymes, whereas the average biodegradation of charged chemicals is 1 to 2 orders of magnitude lower, for which degradation depends more directly on cellular uptake (R 2 ∼ 0.6). Further analysis showed that specific chemical classes such as peptides and organic halogens are relatively less biodegradable in pristine surface waters, resulting in the need for the microbial consortia to acclimate. Additional literature data was used to verify an acclimation model (based on Monod-type kinetics) capable of extrapolating QSAR predictions to acclimating conditions such as in water treatment, downstream lakes and large rivers under μg L -1 to mg L -1 concentrations. The framework developed, despite being based on multiple assumptions, is promising and needs further validation using experimentation with more standardised and homogenised conditions as well as adequate characterization of the inoculum used.

After use and disposal of chemical products, many types of polymer particles end up in the aquatic environment with potential toxic effects to primary producers like green algae. In this study, we have developed Quantitative Structure-Activity Relationships (QSARs) for a set of highly structural diverse polymers which are capable to estimate green algae growth inhibition (EC50). The model (N = 43, R 2  = 0.73, RMSE = 0.28) is a regression-based decision tree using one structural descriptor for each of three polymer classes separated based on charge. The QSAR is applicable to linear homo polymers as well as copolymers and does not require information on the size of the polymer particle or underlying core material. Highly branched polymers, non-nitrogen cationic polymers and polymeric surfactants are not included in the model and thus cannot be evaluated. The model works best for cationic and non-ionic polymers for which cellular adsorption, disruption of the cell wall and photosynthesis inhibition were the mechanisms of action. For anionic polymers, specific properties of the polymer and test characteristics need to be known for detailed assessment. The data and QSAR results for anionic polymers, when combined with molecular dynamics simulations indicated that nutrient depletion is likely the dominant mode of toxicity. Nutrient depletion in turn, is determined by the non-linear interplay between polymer charge density and backbone flexibility., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

To investigate processes possibly underlying accumulation and ecological effects of plastic nano-particles we have characterized their interaction with the cell wall of green algae. More specifically, we have investigated the influence of particle surface functionality and water hardness (Ca 2+ concentration) on particle adsorption to algae cell walls. Polystyrene nanoparticles with different functional groups (non-functionalized, -COOH and -NH 2 ) as well as coated (starch and PEG) gold nanoparticles were applied in these studies. Depletion measurements and atomic force microscopy (AFM) showed that adsorption of neutral and positively charged plastic nanoparticles onto the cell wall of P. subcapitata was stronger than that of negatively charged plastic particles. Results indicated that binding affinity is a function of both inter-particle and particle-cell wall interactions which are in turn influenced by the medium hardness and particle concentration. Physicochemical modelling using DLVO theory was used to interpret the experimental data, using also values for interfacial surface free energies. Our study shows that material properties and medium conditions play a crucial role in the rate and state of nanoparticle bio-adsorption for green algae. The results show that the toxicity of nanoparticles can be better described and assessed by using appropriate dose metrics including material properties, complexation/agglomeration behavior and cellular attachment and adsorption. The applied methodology provides an efficient and feasible approach for evaluating potential accumulation and hazardous effects of nanoparticles to algae caused by particle interactions with the algae cell walls., (Copyright © 2016 Elsevier B.V. All rights reserved.)

In this study, the removal of pharmaceuticals from wastewater as predicted by SimpleTreat 4.0 was evaluated. Field data obtained from literature of 43 pharmaceuticals, measured in 51 different activated sludge WWTPs were used. Based on reported influent concentrations, the effluent concentrations were calculated with SimpleTreat 4.0 and compared to measured effluent concentrations. The model predicts effluent concentrations mostly within a factor of 10, using the specific WWTP parameters as well as SimpleTreat default parameters, while it systematically underestimates concentrations in secondary sludge. This may be caused by unexpected sorption, resulting from variability in WWTP operating conditions, and/or QSAR applicability domain mismatch and background concentrations prior to measurements. Moreover, variability in detection techniques and sampling methods can cause uncertainty in measured concentration levels. To find possible structural improvements, we also evaluated SimpleTreat 4.0 using several specific datasets with different degrees of uncertainty and variability. This evaluation verified that the most influencing parameters for water effluent predictions were biodegradation and the hydraulic retention time. Results showed that model performance is highly dependent on the nature and quality, i.e. degree of uncertainty, of the data. The default values for reactor settings in SimpleTreat result in realistic predictions., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Due to increasing application, release of nanoparticles (NPs) and nanomaterials into the environment becomes likely. Knowledge about NP uptake in organisms is crucial for risk assessment including estimations on the behavior of NPs based on their physicochemical properties. In the present study, the authors have applied current scientific knowledge to construct a mathematical model, which estimates the transport of NPs through a model biological membrane. The semi-empirical model developed showed all parameters studied to substantially affect the agglomeration of the NPs in suspension, thereby also affecting passive transport. The authors quantified the effects of pH, ionic strength, organic matter concentration of medium, and NP size of several inorganic NPs on the permeation through the lipid membrane. Model outcomes and experimental results described in literature were strongly correlated for several metal oxide NPs. With caution, the model may be used to explain some of the existing variance in nano-uptake and toxicity experiments., (© 2014 SETAC.)

We studied the interactions of silica and titanium dioxide nanoparticles with phospholipid membranes and show how electrostatics plays an important role. For this, we systematically varied the charge density of both the membranes by changing their lipid composition and the oxide particles by changing the pH. For the silica nanoparticles, results from our recently presented fluorescence vesicle leakage assay are combined with data on particle adsorption onto supported lipid bilayers obtained by optical reflectometry. Because of the strong tendency of the TiO2 nanoparticles to aggregate, the interaction of these particles with the bilayer was studied only in the leakage assay. Self-consistent field (SCF) modeling was applied to interpret the results on a molecular level. At low charge densities of either the silica nanoparticles or the lipid bilayers, no electrostatic barrier to adsorption exists. However, the adsorption rate and adsorbed amounts drop with increasing (negative) charge densities on particles and membranes because of electric double-layer repulsion, which is confirmed by the effect of the ionic strength. SCF calculations show that charged particles change the structure of lipid bilayers by a reorientation of a fraction of the zwitterionic phosphatidylcholine (PC) headgroups. This explains the affinity of the silica particles for pure PC lipid layers, even at relatively high particle charge densities. Particle adsorption does not always lead to the disruption of the membrane integrity, as is clear from a comparison of the leakage and adsorption data for the silica particles. The attraction should be strong enough, and in line with this, we found that for positively charged TiO2 particles vesicle disruption increases with increasing negative charge density on the membranes. Our results may be extrapolated to a broader range of oxide nanoparticles and ultimately may be used for establishing more accurate nanoparticle toxicity assessments and drug-delivery systems.