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https://www.selleckchem.com/products/kartogenin.html Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM2.5 concentrations at twelve out of the eighteen locations.Air pollution is well recognized as a central player in cardiovascular disease. Exhaust particulate from diesel engines (DEP) is rich in nanoparticles and may contribute to the health effects of particulate matter in the environment. Moreover, diesel soot emitted by modern engines denotes defective surfaces alongside chemically-reactive sites increasing soot cytotoxicity. We recently demonstrated that engineered nanoparticles can cross the air/blood barrier and are capable to reach the heart. We hypothesize that DEP nanoparticles are pro-arrhythm

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