Traffic-related air pollutants (TRAPs) emitted from vehicle tailpipes disperse into nearby microenvironments, posing potential exposure risks. Thus, accurately identifying the emission hotspots of TRAPs is essential for assessing potential exposure risks. We investigated the relationship between turbulent kinetic energy () and pollutant dispersion () through an integrated field measurement. A five-year near-road sampling campaign (5 min based) near a light-duty vehicle-restricted roadway and an on-road sampling campaign (5 s based) on isolated proving grounds were conducted. The was first calculated based on vehicle emission and pollutant concentrations and then paired with measurements. Here, 198 near-road and 377 on-road measurement pairs were collected. In the near-road measurements, and showed a positive relationship ( ≥ 0.69) with the vehicle flow rate, while they showed similar decay patterns and sensitivity to vehicle types in the on-road measurements. A relationship between and (-) was developed through these measurements, demonstrating a robust correlation ( ≥ 0.61) and consistent slope values (1.1-1.3). These findings provide field evidence for the positive association between and , irrespective of the measurement techniques or locations. The - relationship enables vehicle emission estimation with as the sole input, facilitating the identification of emission hotspots with high spatiotemporal resolution.
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http://dx.doi.org/10.1021/acs.est.4c04217 | DOI Listing |
Fluids Barriers CNS
January 2025
Medical Image Processing Department, CHU Amiens-Picardie University Hospital, Amiens, France.
Background: The pressure gradient between the ventricles and the subarachnoid space (transmantle pressure) is crucial for understanding CSF circulation and the pathogenesis of certain neurodegenerative diseases. This pressure can be approximated by the pressure difference across the aqueduct (ΔP). Currently, no dedicated platform exists for quantifying ΔP, and no research has been conducted on the impact of breathing on ΔP.
View Article and Find Full Text PDFBMC Psychol
January 2025
Department of Medical Psychology, Air Force Medical University, Xi'an, China.
Purpose: The purpose of this study was to use the advanced technique of Network Intervention Analysis (NIA) to investigate the trajectory of symptom change associated with the effects of self-control training on youth university students' chronic ego depletion aftereffects.
Methods: The nine nodes of chronic ego depletion aftereffects and integrated self-control training were taken as nodes in the network and analyzed using NIA. Networks were computed at the baseline, at the end of treatment, at 1-, 3-, 6-, 9- and 12-month follow up.
BMC Oral Health
January 2025
National Center for Professional Training, Ministry of Health and Medical Education, Tehran, Iran.
Background: Maintenance of oral health, prevention, and health promotion stand as primary competencies for dental graduates. Consequently, it is necessary to promote such an approach in dental schools, which are traditionally focused on treatment, to improve the attitude and practice of students in the field of prevention, the final result of which is the reduction of oral and dental diseases in patients. The study aimed to design Integrated Oral Health Care Pathways (IOHCPs) for adults and children referred to Tehran University of Medical Sciences (TUMS), School of Dentistry.
View Article and Find Full Text PDFNat Food
January 2025
School of Biological Sciences, University of Canterbury, Christchurch, New Zealand.
For commercial viability, cultivated meats require scientifically informed approaches to identify and manage hazards and risks. Here we discuss food safety in the rapidly developing field of cultivated meat as it shifts from lab-based to commercial scales. We focus on what science-informed risk mitigation processes can be implemented from neighbouring fields.
View Article and Find Full Text PDFJ Intellect Disabil
January 2025
Pro Vice Chancellor, Staffordshire University, UK.
Background: Autism spectrum disorder poses challenges in social communication and behavior, while Intellectual disabilities are characterized by deficits in cognitive, social, and adaptive skills, frequently accompanied by stereotypies and challenging behaviors. Despite the progress made in autism spectrum disorder research, there is often a lack of research focusing on individuals with co-occurring autism spectrum disorder and intellectual disability. Robot-assisted autism therapies are effective in addressing these needs.
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