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Polycyclic aromatic hydrocarbons and cannabinoids in secondhand cannabis smoke

The legalization of cannabis is exposing more people to secondhand smoke (SHS) generated during cannabis use. Given the serious health effects caused by tobacco SHS, there is a need to assess the potential health effects of exposure to cannabis SHS. As a step toward this, we measured the concentrations of cannabinoids, nicotine and polycyclic aromatic hydrocarbons (PAHs) in air samples collected in public places where cannabis was being consumed. These were compared with concentrations in exhaled aerosols from cannabis smoking and vaping, and in tobacco SHS. Tetrahydrocannabinol concentrations were 22 to 255 µg/m 3 in field samples, below the threshold for psychoactive effects. Nicotine concentrations in field samples did not exceed 1 µg/m 3 . The total PAH concentrations in field samples were from 3.2 to 80.5 ng/m 3 , depending on location type. By contrast, PAH levels averaged 72 ng/m 3 in tobacco SHS and 220 ng/m 3 in the more concentrated, exhaled cannabis aerosols. A total of 22 different PAHs were identified in field samples of cannabis aerosols, from which benz[a]anthracene (B[a]A) was present in the highest concentrations. The PAH profile of cannabis aerosols was different from that of tobacco SHS. A preliminary cancer risk evaluation showed that the dose associated with inhalation of cannabis SHS during an 8-h work shift exceeded the California No Significant Risk Level for B[a]A at all venues where cannabis was consumed primarily via smoking. In summary, the consumption of cannabis, by smoking and by vaporizing, can create aerosols that contain carcinogenic PAHs. Thus breathing secondhand cannabis aerosols increases exposure to carcinogens.

Aerosol↗

Secondhand Exposure to Simulated Cannabis Vaping Aerosols

Emissions from cannabis vaping degrade indoor air quality and expose non-users to secondhand pollutants. We investigated how the vaping mixture composition affects indoor aerosol characteristics and exposures. Simulated cannabis vaping aerosol was produced by flash evaporation in a 20 m3 chamber of mixtures containing terpenoids, cannabinoids, cannabis extract constituents, and the adulterant vitamin E acetate (VEA). Aerosol time- and size-resolved concentrations (8 nm-2.5 μm at 1 Hz) were measured, and a dosimetry model was used to evaluate the intake of secondhand aerosols. The results showed peak particle number (PN) concentrations between 0.7 × 106 and 13 × 106 cm-3 and peak mass concentration (PM1.0) between 65 and 1191 μg m-3 at t = 5 min after emission. Concentrations decreased to 21-57% of peak PN and 33-69% of peak PM1.0 at t = 60 min. The PM1.0 yield was 0.06 for a terpenoid-only mixture, 0.22-0.36 for tetrahydrocannabinol (THC)-terpenoid mixtures, and >1 for mixtures containing high concentrations of cannabidiol (CBD) or VEA. For intake deposition, the highest aerosol fraction was deposited in the pulmonary region, followed by the tracheobronchial and head regions. Deposition increased in the presence of THC, CBD, or VEA, with aerosols <100 nm contributing the majority of particles deposited in all regions.

Tang, Xiaochen↗

Coupled cluster theory on modern heterogeneous supercomputers

This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory—a linear-scaling, massively parallel framework—as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗