The aim of this study was to evaluate the mobility of platinum (Pt) and palladium (Pd) emissions from automotive catalysts in soils and to contribute to the risk assessment of platinum group metals (PGMs) discharged from catalysts in the environment. To address this question, for the first time risk assessment code (RAC) was applied to consider the results from sequential extraction of different Pd and Pt species from soils. For this purpose, model soil samples were prepared spiking defined Pd or Pt species, respectively, at known concentrations. In order to mimic emitted species as well as possible transformation products of traffic-related Pd and Pt emissions in soils, coated and uncoated elemental nanoparticles (cPd/cPt NPs, Pd/Pt NPs) and ionic divalent metal species (Pd(II)/Pt(II)) were applied. All model samples were characterized in detail and the developed sequential extraction scheme was validated. RAC values ranged between 24 and 8% revealing medium to low risk. The order of mobility for the studied species was found to be Pt(II) > cPd NPs » Pd(II) > Pd NPs > Pt NPs > cPt NPs. Furthermore, migration of Pd species in gravity columns was studied confirming highest transport of cPd NPs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.envpol.2018.07.130 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph.
Sensors (Basel)
December 2024
School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK.
Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood.
View Article and Find Full Text PDFImproving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.
Sensors (Basel)
December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
View Article and Find Full Text PDFImprovement of Selected Quality and Safety Traits in Turmeric-Enriched Kale Pesto Using Blue Light and Sous-Vide.
Molecules
December 2024
Department of Chemistry and Biodynamics of Food, Institute of Animal Reproduction and Food Research of Polish Academy of Sciences, Juliana Tuwima 10, 10-748 Olsztyn, Poland.
The potential of blue light (BL) and sous-vide (S-V) as a novel approach for food preservation was investigated via measurements of the total phenolic content (TPC), antioxidative activity, color, and their antibacterial effect on in two versions of laboratory-prepared kale pesto, with and without the addition of turmeric. The TPC ranged from 85 to 208 mg/100 g GAE d.m.
View Article and Find Full Text PDFEffects of Sequential Antimicrobial Phases on Root Canal Microbiome Dynamics in Two-Visit Treatment of Primary Apical Periodontitis: A Longitudinal Experimental Study.
Life (Basel)
December 2024
Department of Microbiology, Faculty of Dentistry, Nuh Naci Yazgan University, Kayseri 38170, Turkey.
Background: Effective management of primary apical periodontitis depends on understanding the dynamic interactions within the root canal microbiome. This study aimed to investigate the effect of sequential antimicrobial phases on the root canal microbiome during a two-visit treatment approach, with a focus on calcium hydroxide medication.
Methods: Samples were collected from three teeth across four treatment phases: initial infection (S1), after chemomechanical preparation (S2), after intracanal medication (S3), and after a final flush (S4).
Outlier Handling Strategy of Ensembled-Based Sequential Convolutional Neural Networks for Sleep Stage Classification.
Bioengineering (Basel)
December 2024
Jiangsu Key Laboratory of Intelligent Medical Image Computing, Nanjing 210044, China.
The pivotal role of sleep has led to extensive research endeavors aimed at automatic sleep stage classification. However, existing methods perform poorly when classifying small groups or individuals, and these results are often considered outliers in terms of overall performance. These outliers may introduce bias during model training, adversely affecting feature selection and diminishing model performance.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!