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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 = 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 ∼ 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 ∼ 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 to mg L 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.

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.

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.

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Polymer electrolyte fuel cell (PEFC) membranes are subject to radical-induced degradation. Antioxidant strategies for hydrocarbon-based ionomers containing aromatic units can focus on intermediates that are formed upon attack by hydroxyl radicals (HO • ). Among the different intermediates, the cation radical P •+ is the most promising target for repair, for example by cerium(III). For the "repair" reaction of Ce(III) with radicals of a poly(α-methylstyrene sulfonate) oligomer we determined an activation energy of (9 ± 2) kJ mol −1 and a rate constant of 1.6 • 10 8 M −1 s −1 at 80°C by pulse-radiolysis. For the reduction of Ce(IV) by hydrogen peroxide the activation energy was determined by stopped-flow as (30 ± 1) kJ mol −1 with a rate constant of 4.8 • 10 6 M −1 s −1 at 80°C. These parameters are fed into a kinetics model to estimate the efficacy of the cerium (III)/(IV) redox couple as a catalytic repair agent in hydrocarbon-based fuel cell membranes. While cerium can mitigate polymer degradation, repair efficacy depends on the polymer degradation pathway and the nature and lifetime of the intermediates.

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.