This paper presents data from a survey of polychlorinated biphenyls (PCBs) and hexachlorobenzene (HCB) concentrations in 191 global background surface (0-5 cm) soils. Differences of up to 4 orders of magnitude were found between sites for PCBs. The lowest and highest PCB concentrations (26 and 97,000 pg/g dw) were found in samples from Greenland and mainland Europe (France, Germany, Poland), respectively. Background soil PCB concentrations were strongly influenced by proximity to source region and soil organic matter (SOM) content Most (>80%) of the estimated soil PCB burden remains in the "global source region" of the Northern Hemisphere (NH) temperate latitudes (30-60 degrees N) or in the OM-rich soils just north of that %SOM correlated with PCB and HCB in the global data set, with the correlation coefficients being greater for HCB and the lighter PCBs than for heavier homologues. OM-rich soils in the NH consistently contained the highest burdens; such soils are a key global compartment for these compounds. Evidence for global fractionation of PCBs was found in the subset of soils from latitudes north of the global source region but was not discerned with the global data set The full data set was used to estimate the burden for individual congeners/homologues in surface background soils and a global soil total PCB burden of 21,000 t. The significance of the inventory is briefly discussed in relation to the latest estimates of global production and atmospheric emission.
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http://dx.doi.org/10.1021/es025809l | DOI Listing |
J Chem Theory Comput
January 2025
Preferred Networks, Inc., Tokyo 100-0004, Japan.
Mapping the chemical reaction pathways and their corresponding activation barriers is a significant challenge in molecular simulation. Given the inherent complexities of 3D atomic geometries, even generating an initial guess of these paths can be difficult for humans. This paper presents an innovative approach that utilizes neural networks to generate initial guesses for reaction pathways based on the initial state and learning from a database of low-energy transition paths.
View Article and Find Full Text PDFBioelectromagnetics
January 2025
Seibersdorf Labor GmbH, Seibersdorf, Austria.
The electrical conductivity of human tissues is a major source of uncertainty when modelling the interactions between electromagnetic fields and the human body. The aim of this study is to estimate human tissue conductivities in vivo over the low-frequency range, from 30 Hz to 1 MHz. Noninvasive impedance measurements, medical imaging, and 3D surface scanning were performed on the forearms of ten volunteer test subjects.
View Article and Find Full Text PDFRadiat Oncol
January 2025
Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
Background: Several studies have suggested that lung tissue heterogeneity is associated with overall survival (OS) in lung cancer. However, the quantitative relationship between the two remains unknown. The purpose of this study is to investigate the prognostic value of whole lung-based and tumor-based radiomics for OS in LA-NSCLC treated with definitive radiotherapy.
View Article and Find Full Text PDFBMC Complement Med Ther
January 2025
Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
Background: Currently, there is no agreed-upon data collection tool for comprehensively structured documentation of Iranian traditional medicine (ITM) from the information management perspective. As ITM practice varies significantly from current medicine in diagnosis and treatment approaches, it is not appropriate to use data platforms or information systems developed for current medicine. Consequently, the collected data are non-comparable, reducing the verdicts' generalization.
View Article and Find Full Text PDFSci Data
January 2025
Division of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, 20010, USA.
Proper personal protective equipment (PPE) use is critical to prevent disease transmission to healthcare providers, especially those treating patients with a high infection risk. To address the challenge of monitoring PPE usage in healthcare, computer vision has been evaluated for tracking adherence. Existing datasets for this purpose, however, lack a diversity of PPE and nonadherence classes, represent single not multiple providers, and do not depict dynamic provider movement during patient care.
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