Some metal-based engineered nanoparticles (ENPs) undergo fast dissolution and/or aggregation when they are released in the environment. The underlying processes are controlled by psychochemical/biological parameters of the environment and the properties of the particles. In this study, we investigated the interaction between algal cells and zero valent copper nanoparticles (Cu0-ENPs) to elucidate how the cells influence the dissolution and aggregation kinetics of the particles and how these kinetics influence the cellular uptake of Cu. Our finding showed that the concentration of dissolved Cu ([Cu]dissolved) in the supernatant of the culture media without algal cells was higher than the [Cu]dissolved in the media with algal cells. In the absence of the cells, dissolved organic matter (DOC) increased the dissolution of the particle due to increasing the stability of the particles against aggregation, thus increasing the available surface area. In the presence of algae, Cu0-ENPs heteroaggregated with the cells. Thus, the available surface area decreased over time and this resulted in a low dissolution rate of the particles. The DOC corona on the surface of the particles increased the heteroaggregation of the particles with the cells and decreases the uptake of the particles. Our findings showed that microorganisms influence the fate of ENPs in the environment, and they do so by modifying the dissolution and aggregation kinetics of the Cu0-ENPs.

Ecological risk assessments are hampered by limited availability of ecotoxicity data. The present study aimed to explore the possibility of deriving SSD (species sensitivity distribution) parameters for non-tested compounds, based on simple physicochemical characteristics, known SSDs for data-rich compounds and a 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 (either from acute EC50 s or chronic NOECs). Beyond exploratory uses, we discussed 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. This article is protected by copyright. All rights reserved.

Hydroxyl radicals (·OH) initiated degradation is an important process governing fate of aquatic organic micropollutants (OMPs). However, rate constants for aqueous reaction of OMPs with ·OH (kOH) are available only for a limited number of OMPs, which complicates fate assessment of OMPs. Furthermore, molecular structures of many OMPs contain ionizable groups, and the OMPs may dissociate into different anionic/cationic species with different reactivity towards ·OH. Therefore, it is of importance to determine kOH of ionizable OMPs, and to develop quantitative structure-activity relationship (QSAR) models for predicting kOH of OMPs at different ionization forms. Herein kOH values of 9 fluoroquinolones (FQs) and 11 sulfonamides (SAs) at 3 dissociation forms (FQ±/FQ+/FQ-, SA0/SA+/SA-) were determined by competition kinetics experiments. A QSAR model using theoretical molecular structural descriptors was subsequently developed. The QSAR model successfully corroborated previous experimental results, exhibited good statistical performance, and is capable to predict kOH for FQs and SAs with different dissociation forms at environmentally relevant pH conditions. As organic ions have rarely been included in previous QSAR studies, the newly developed model that covers both neutral molecules and ions is of significance for future QSAR development as well as fate assessment of ionizable OMPs.