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(1) Background: Mathematical exposure modeling of volatile organic compounds (VOCs) in consumer spray products mostly assumes instantaneous mixing in a room. This well-mixed assumption may result in the uncertainty of exposure estimation in terms of spatial resolution. As the inhalation exposure to chemicals from consumer spray products may depend on the spatial heterogeneity, the degree of uncertainty of a well-mixed assumption should be evaluated under specific exposure scenarios. (2) Methods: A room for simulation was divided into eight compartments to simulate inhalation exposure to an ethanol trigger and a propellant product. Real-time measurements of the atmospheric concentration in a room-sized chamber by proton transfer reaction mass spectrometry were compared with mathematical modeling to evaluate the non-homogeneous distribution of chemicals after their application. (3) Results: The well-mixed model overestimated short-term exposure, particularly under the trigger spray scenario. The uncertainty regarding the different chemical proportions in the trigger did not significantly vary in this study. (4) Conclusions: Inhalation exposure to aerosol generating sprays should consider the spatial uncertainty in terms of the estimation of short-term exposure.

Developing efficient and cost-effective materials is crucial for advancing the electrochemical oxygen reduction reaction (ORR). By introducing Co2+ ions into formamide, our method prevents rapid Fe2+ oxidation to Fe3+, promoting the formation of well-defined Fe3O4 nanoparticles rather than Fe2O3. This study presents a synthesis route for high-performance spinel Fe and Co oxide nanoparticles on N-doped reduced graphene oxide (NRGO). This solvothermal synthesis in formamide yields well-dispersed, ultrafine FeCo(OH)x nanoparticles (∼5 nm) anchored on NRGO. These nanoparticles can be employed for the formation of spinel FexCo3−xO4 oxide nanoparticles, potentially due to their high surface area and strong interaction with the NRGO support. This, in turn, facilitates the successful decoration of highly dispersed spinel FexCo3−xO4 oxide nanoparticles (∼30 nm) onto the NRGO support, even after calcination at 900 °C, which represents the critical temperature for conventional graphitization. This unique approach results in significantly reduced particle aggregation compared with that of conventional methods. The (Co)Fe3O4-NRGO nanocomposite exhibits remarkable ORR activity, achieving an electron number of ∼3.7 and a current density of 5.01 mA cm−2 at E = 0.75 VRHE, comparable to those of commercial Pt/C catalysts. Furthermore, the catalyst exhibits remarkable stability, maintaining a reducing current density that is 42% lower after 40 000 s of uninterrupted operation at 0.75 VRHE compared with a 75% reduction observed with Pt/C. This exceptional performance is attributed to the strong interaction between the (Co)Fe3O4 nanoparticles and NRGO, facilitated by the Co ion precursor during annealing. [ABSTRACT FROM AUTHOR]

This study employed an integrated field monitoring approach, combining chemical analysis and ecotoxicity testing of multiple environmental matrices—water, sediment, and sediment elutriates—to comprehensively assess the environmental health of the Andong watershed, located near a Zn smelter and mining area. The primary objectives were to evaluate the extent of metal contamination, identify key toxicants contributing to ecological degradation, and trace the sources of these pollutants. Our findings revealed severe metal contamination and significant ecotoxicological effects both in proximity to and downstream from industrial sites. Specifically, Cd, Zn, and Pb were strongly linked to the smelter, while Hg, Ni, Cu, and As were predominantly associated with mining activities in the tributaries. To further assess toxicity of field-collected sediment and their elutriates, a logistic regression analysis was employed to estimate benchmark values for distinguishing between toxic and non-toxic samples, using the sum of toxic units for sediment elutriates and the mean probable effect level (PEL) quotient for sediment toxicity. These models demonstrated greater predictive accuracy than conventional benchmarks for determining toxicity thresholds. Our results highlight that integrating chemical and ecotoxicological monitoring with site-specific concentration–response relationships enhances the precision of ecological risk assessments, facilitating more accurate identification of key toxicants driving mixture toxicity in complex, pollution-impacted aquatic ecosystems. [ABSTRACT FROM AUTHOR]

The commercialization of Li-metal batteries is primarily hindered by the poor stability and safety risks associated with the use of Li-metal anodes. As an alternative, research is being conducted to improve the stability of anodes using Li–Mg alloys. However, after Li stripping, Li-poor Li–Mg alloys lower the usability of Li; moreover, the mechanism by which the cycling characteristics improve when the grain boundary density increases remains obscure. In this study, we controlled the grain size of Li–Mg alloys by adjusting the cold rolling and solidification rates, and investigated the effect of grain boundaries on the deposition and stripping characteristics of Li+ ions. Thus, an Li-rich Li–Mg phase was formed at the grain boundaries when casting Li–Mg alloys. As the grain boundary density increased, more sites with preferential Li+ deposition and stripping were observed, leading to a reduction in the overpotential and improved cycling characteristics. An optimized grain size was discovered through symmetric cell tests of the Li–Mg alloys with various grain sizes ranging from 7 to 298 μm, and a superior cycling performance of up to 1500 h (375 cycles) was achieved at the optimal grain size. These results are expected to provide an important foundation for the development of stable and safe Li-metal anodes using Li–Mg alloys. [ABSTRACT FROM AUTHOR]

There is a pressing need for tools that can rapidly predict PM 2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of PM 2.5 concentrations and related health effects. The U-Net model was trained with 2019 South Korean PM 2.5 data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA's BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required PM 2.5 emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce PM 2.5 -related health risks and estimate economic benefits. [ABSTRACT FROM AUTHOR]