Sunscreens are an important source of TiO₂ nanoparticles in surface waters.

Dissolved organic matter (DOM) present in surface aquatic systems is a heterogeneous mixture of organic compounds reflecting its allochthonous and autochthonous organic matter (OM) sources. The composition of DOM is determined by environmental factors like land use, water chemistry, and climate, which influence its release, movement, and turnover in the ecosystem. However, studying the impact of these environmental factors on DOM composition is challenging due to the dynamic nature of the system and the complex interactions of multiple environmental factors involved. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) enables detailed molecular-level analysis of DOM, allowing the identification of thousands of individual molecular formulas potentially representing unique markers for its "molecular history". The combination of FT-ICR MS with machine-learning techniques is promising to unravel DOM-environment interactions owing to their capacity to capture complex non-linear relationships. We present a novel unsupervised multi-variant machine-learning approach, aiming to model correlation coefficients as robust indicators of how changes in environmental factors (e.g., the concentration of nutrients or the land use) result in changes in the molecular formula descriptors of DOM (i.e., aromaticity index or hydrogen to carbon ratio). We applied this approach to an environmental data set collected from 84 sites across central Europe exhibiting a broad range of water chemistry and land uses. Our model revealed an increase in molecular mass and aromaticity of DOM in densely forested regions as compared to open urban areas, where DOM was characterized by higher concentrations of dissolved ions and increased microbial degradation, leading to smaller and more aliphatic DOM. Our findings highlight the substantial human impact on climate change, as evidenced by the accelerated photochemical and microbial degradation of DOM, which consequently enhances greenhouse gas emissions and exacerbates global warming.

Riverbank filtration systems are important structures that ensure the cleaning of infiltrating surface water for drinking water production. In our study, we investigated the potential risk for a breakthrough of environmentally aged silver nanoparticles (Ag NP) through these systems. Additionally, we identified factors leading to the remobilization of Ag NP accumulated in surficial sediment layers in order to gain insights into remobilization mechanisms. We conducted column experiments with Ag NP in an outdoor pilot plant consisting of water-saturated sediment columns mimicking a riverbank filtration system. The NP had previously been aged in river water, soil extract, and ultrapure water, respectively. We investigated the depth-dependent breakthrough and retention of NP. In subsequent batch experiments, we studied the processes responsible for a remobilization of Ag NP retained in the upper 10 cm of the sediments, induced by ionic strength reduction, natural organic matter (NOM), and mechanical forces. We determined the amount of remobilized Ag by ICP-MS and differentiated between particulate and ionic Ag after remobilization using GFAAS. The presence of Ag-containing heteroaggregates was investigated by combining filtration with single-particle ICP-MS. Single and erratic Ag breakthrough events were mainly found in 30 cm depth and Ag NP were accumulated in the upper 20 cm of the columns. Soil-aged Ag NP showed the lowest retention of only 54%. Remobilization was induced by the reduction of ionic strength and the presence of NOM in combination with mechanical forces. The presence of calcium in the aging- as well as the remobilizing media reduced the remobilization potential. Silver NP were mainly remobilized as heteroaggregates with natural colloids, while dissolution played a minor role. Our study indicates that the breakthrough potential of Ag NP in riverbank filtration systems is generally low, but the aging in soil increases their mobility. Remobilization processes are associated to co-mobilization with natural colloids.

According to the current European regulation on cosmetics, any ingredient present as a nanomaterial should be indicated in the ingredient list. There is a need for analytical methods capable of determining the size of the relevant ingredients and thus assessing if these are nanomaterials or not. An analytical method based on asymmetric flow field-flow fractionation (AF4) and inductively coupled plasma-mass spectrometry (ICP-MS) was developed to determine the size of particles present in a commercial toothpaste. Multi-angle light scattering (MALS) was used for on-line size determination. The number-based particle size distributions (PSDs) of the particles were retrieved upon mathematical conversion of the mass-based PSDs recovered from the AF4-ICP-MS fractograms. AF4-ICP-MS allowed to separate and detect Al₂O₃ and TiO₂ particles in the toothpaste and to retrieve a correct TiO₂ number-based PSD. The potential presence of particles in the lower size range of the Al₂O₃ mass-based PSD had a strong impact on sizing and nanomaterial classification upon conversion. AF4 coupled with ICP-MS and MALS was found to be a powerful approach for characterization of different particles in a multiple-particle system such as toothpaste. Confirmation of particle size by a secondary method such as single particle ICP-MS or hydrodynamic chromatography was crucial.