Differential aggregation of polystyrene and titanium dioxide nanoparticles under various salinity conditions and against multiple proteins types

The interaction of nanoplastics (NPls) and engineered nanoparticles (ENPs) with organic matter and environmental pollutants is particularly important. Therefore, their behavior should be investigated under the different salinity conditions, mimicking rivers and coastal environments, to understand this phenomenon in those areas. In this work, we analyzed the elementary characteristics of polystyrene-PS (unmodified surface and modified with amino or carboxyl groups) and titanium dioxide-TiO2 nanoparticles. The effect of salinity on their colloidal properties was studied too. Also, the interaction with different types of proteins (bovine serum albumin-BSA and tilapia proteins), as well as the formation of the BSA corona and its effect on the colloidal stability of nanoparticles, were evaluated. The morphology and dispersion of sizes were more uniform in unmodified-surface PS-NPs (70.5?±?13.7 nm) than in TiO2-NPs (131.2?±?125.6 nm). Likewise, Rama spectroscopy allowed recognizing peaks associated with the PS phenyl group aromatic ring in unmodified-surface PS-NPs (621, 1002, 1582, and 1602 cm^?1). For TiO2-NPs, the data suggest belonging to the tetragonal form, also known as rutile (445, 610 cm^?1). The elevation of salinity dose-dependently decreased NP colloid stability, with more significant variation in the PS-NPs compared to TiO2-NPs. The organic matter is also involved in this phenomenon, differentially as a function of time compared to its absence (unmodified-surface PS-NPs 30 psu/TOC 5 mgL^?1/24 h: 2876.6?±?378.03 nm; unmodified-surface PS-NPs 30 psu/24 h: 2133?±?49.57 nm). In general, the TiO2-NPs demonstrated greater affinity with all proteins tested (0.066 g/L). It was observed that morphology, size, and surface chemical modification intervene in a relevant way in the interaction of the nanoparticles with bovine serum albumin (unmodified-surface PS-NPs 298 K: 6.08E⁺⁰²; 310 K: 6.63E⁺⁰²; TiO2-NPs 298 K: 8.76E⁺⁰²; 310 K: 1.05E⁺⁰³ L mol^?1) and tilapia tissues proteins (from blood, gills, liver, and brain). Their morphology and size also determined the protein corona formation and the NPs’ agglomeration. These findings can provide references during knowledge transfer between NPls and ENPs.

     

Exploring the embodied carbon flow interactive relationships in China from an ecological network perspective: a model framework and application at provincial level

Environmental Science and Pollution Research - Energy-related carbon emissions take a large proportion in China, and the interregional trade caused by provincial disparities has led to significant...     

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

Environmental Science and Pollution Research - To assess the status of hotspots and research trends on geographic information system (GIS)–based landslide susceptibility (LS), we analysed...     

Light absorption enhancement of particulate matters and their source apportionment over the Asian continental outflow site and South Yellow Sea

Environmental Science and Pollution Research - Light absorption enhancement of black carbon due to the aerosol mixing states is an important parameterization for climate modeling, while emission...     

TiO2 photocatalytic degradation of PCBs in soil-water systems containing fluoro surfactant

Titanium dioxide-mediated photodegradation of Polychlorinated biphenyls (PCBs) in soil/aqueous systems with added fluorinated surfactant was investigated. PCBs can bind tightly to organic matter in the soil, especially in aged, contaminated soil. Experiments showed an effective PCB photocatalytic degradation in mixed systems of soil/clay with anionic fluorinated surfactant FC-143 and TiO2. The FC-143 surfactant is stable in this photochemical process. PCB degradation rates in samples followed the order: spiked clay > spiked soil > Hudson River bank soil. The results suggest that anionic fluorinated surfactant may form semimicelles and/or admicelles on the surface of positively charged TiO2. The hydrophobic surface of TiO2 can provide a nonpolar phase that acts as a partioning medium for hydrophobic PCBs. Therefore, PCBs in soil can be released to the semimicelle and/or admicelle on the TiO2 surface and are effectively photodegraded in a dispersion containing anionic fluorinated surfactant. The combination of surfactant extraction and photooxidation forms the basis for a novel two-stage process for the removal and destruction of PCBs from soil.     

Biodegradation of 2,4,6-trichlorophenol in the presence of primary substrate by immobilized pure culture bacteria

In this study, pure strains that are capable of utilizing 2,4,6-trichlorophenol have been isolated from the mixed culture grown on substrates containing chlorophenolic compounds. Studies have been carried out on the capability of these isolated pure strains in suspended and immobilized forms to decompose 2,4,6-trichlorophenol. Additionally, the influence of primary substrates (e.g., phenol, 2-chlorophenol, 3-chlorophenol, 4-chlorophenol, 2,4-dichlorophenol) on the decomposition of 2,4,6-trichlorophenol by the isolated pure strains grown in immobilized form is also investigated. The results are: Through bacterial isolation and identification, three pure strains have been obtained: Pseudomonas spp. strain 01, Pseudomonas spp. strain 02 and Agrobacterium spp. Whether in suspended or immobilized forms, all strains have poor removal efficiencies of 2,4,6-trichlorophenol. However, addition of 200 mg/l phenol will enable the immobilized Pseudomonas spp. strain 01, and Pseudomonas spp. strain 02 to achieve 65% and 48% removal of 2,4,6-trichlorophenol, respectively. Addition of phenol will assist the immobilized Pseudomonas spp. strain 02 in achieving removal of 2,4,6-trichlorophenol but the removal efficiency is not good if the phenol concentration is too low. The optimum phenol concentration should be between 200 and 400 mg/l.     

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.     

Effects of green spaces on alleviating mortality attributable to PM2.5 in China

Environmental Science and Pollution Research - Increasing research suggested that green spaces are associated with many health benefits, but evidence for the quantitative relationship between green...     

Comparison of metabolome profiles in zebrafish (Danio rerio) intestine induced by polystyrene microplastics with different sizes

Environmental Science and Pollution Research - Microplastics (MPs) are widespread in aquatic environments. They could induce intestinal toxicity in the fish. However, research on the metabolic...     

Assessing economic sustainability and ecological efficiency with genuine progress indicator: a case study of the Yangtze River Delta from 2000 to 2018

To achieve urban sustainability, it is critical to enhance the environment, economy, and society simultaneously. This study adopted the revised genuine progress indicator (GPI) and ecological footprint (EF) to evaluate the ecological efficiency and economic sustainability of the Yangtze River Delta from 2000 to 2018. Spatial analysis was utilized to identify spatial autocorrelation. A total of 27 cities were then partitioned through k-means cluster analysis. The results showed that GPI and ecological efficiency improved rapidly, but economic sustainability showed a downward trend. GPI and GDP had a high degree of spatial correlation, especially in Suzhou-Wuxi-Changzhou Metropolitan Area. However, no spatial correlation existed between GPI and EF. The city with high GEE can reach 3000 $/gha, indicating the city consumed 1 global hectare to create $3000 of genuine economic growth. Shanghai, Hangzhou, and Taizhou were cities with the highest level of economic sustainability and ecological efficiency. The spatiotemporal characteristics of economic sustainability and ecological efficiency revealed in this study will provide theoretical guidance for alleviating ecological pressure and promoting economic sustainable development.