Fine particulate matters less than 2.5 µm (PM) in the ambient atmosphere are strongly associated with adverse health effects. However, it is unlikely that all fine particles are equally toxic in view of their different sizes and chemical components. Toxicity of fine particles produced from various combustion sources (diesel engine, gasoline engine, biomass burning (rice straw and pine stem burning), and coal combustion) and non-combustion sources (road dust including sea spray aerosols, ammonium sulfate, ammonium nitrate, and secondary organic aerosols (SOA)), which are known major sources of PM, was determined. Multiple biological and chemical endpoints were integrated for various source-specific aerosols to derive toxicity scores for particles originating from different sources. The highest toxicity score was obtained for diesel engine exhaust particles, followed by gasoline engine exhaust particles, biomass burning particles, coal combustion particles, and road dust, suggesting that traffic plays the most critical role in enhancing the toxic effects of fine particles. The toxicity ranking of fine particles produced from various sources can be used to better understand the adverse health effects caused by different fine particle types in the ambient atmosphere, and to provide practical management of fine particles beyond what can be achieved only using PM mass which is the current regulation standard.
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http://dx.doi.org/10.1038/s41598-018-35398-0 | DOI Listing |
Sci Rep
January 2025
Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles.
View Article and Find Full Text PDFEnviron Pollut
January 2025
University of Southern California, Department of Civil and Environmental Engineering, Los Angeles, California, USA. Electronic address:
Airborne particulate matter (PM) in urban environments poses significant health risks by penetrating the respiratory system, with concern over lung-deposited surface area (LDSA) as an indicator of particle exposure. This study aimed to investigate the diurnal trends and sources of LDSA, particle number concentration (PNC), elemental carbon (EC), and organic carbon (OC) concentrations in Los Angeles across different seasons to provide a comprehensive understanding of the contributions from primary and secondary sources of ultrafine particles (UFPs). Hourly measurements of PNC and LDSA were conducted using the DiSCmini and Scanning Mobility Particle Sizer (SMPS), while OC and EC concentrations were measured using the Sunset Lab EC/OC Monitor.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China.
The bioaccessibility of cadmium (Cd) and lead (Pb) in the gastrointestinal tract is crucial for health risk assessments of contaminated soils. However, variability in In vitro analytical conditions and soil properties introduces bias and uncertainty in predictions. This study employed three in vitro methods to measure Cd and Pb bioaccessibility during the gastric and gastrointestinal phases, using soil samples incubated for one year.
View Article and Find Full Text PDFFuture Cardiol
January 2025
Department of Cardiovascular Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
The recently introduced concept of 'exposome' emphasizes the impact of non-traditional threats onto cardiovascular health. Among these, air pollutants - particularly fine particulate matter < 2.5 μm (PM2.
View Article and Find Full Text PDFNature
January 2025
Machine Learning Lab, University of Freiburg, Freiburg, Germany.
Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.
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