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Evaluating exposure to vehicle pollutants using physics-informed immersive reality models. | LitMetric

Evaluating exposure to vehicle pollutants using physics-informed immersive reality models.

R Soc Open Sci

School of Engineering, University of Birmingham, Birmingham B15 2TT, UK.

Published: September 2024

AI Article Synopsis

  • Major health issues arise from unregulated particle pollutants produced by vehicles, especially during braking phases, which pose risks to the public.
  • The study uses advanced simulations to assess local exposure to these pollutants and create an immersive reality experience to help people understand the health risks.
  • Findings indicate that the most significant pollution occurs during rapid deceleration, but moving just 1.5 meters away from cars significantly reduces exposure, making the tool beneficial for guiding urban planning and public health strategies.

Article Abstract

Major health risks and chronic diseases are caused by exposure to unregulated particle pollutants from road, tyre and brake sources. Here, we use large-eddy simulations to identify local exposure to these harmful pollutants and build a physics-informed immersive reality experience to communicate outcomes with the general public for health guidance. Our analysis reveals that exposure to non-exhaust pollution is greatest at the end of braking phases, when deceleration rates are above 3 m s, diminishes to background levels for pedestrians located 1.5 m away from a car, and is reasonably insensitive to the car type. We show that by using immersive reality models to visualize pollution data in a human-centric format, people could identify pollutant sources and health risks, and understand how to navigate urban spaces for reduced exposure. This was achieved without any prerequisite knowledge and with minimal dependency on educational background, suggesting the approach can support public health guidance, policymakers and urban planners towards improving air quality in urban environments.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11421894PMC
http://dx.doi.org/10.1098/rsos.241111DOI Listing

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