Abstract
Due to rapid global economic development, the number of motor vehicles has increased sharply, causing significant traffic pollution and posing a threat to people’s health. People’s exposure to traffic-related particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) primarily occurs during commuting. Many studies have used exposure risk assessment models to assess the possible adverse effects of PM2.5, but few have used them to plan low-risk pathways for commuters. This study simulated the pollutant concentration distribution in an idealized urban area in different scenarios. We then used a back propagation (BP) neural network to predict the pollutant concentration. The commuter respiratory deposition dose was calculated based on the BP prediction results, and the respiratory deposition dose was converted into obstacles on the commuting map. Finally, the rapidly exploring random tree star (RRT*) algorithm was used to plan low-risk paths for commuters. The results indicate that pollutants discharged by cars and tree planting can significantly affect the pollutant concentration. A 30.25 μg/m3 increase in the pollutant concentration discharged by cars resulted in a 7~13 μg/m3 increase in the traffic-related air pollution concentration on sidewalks. Combining a computational fluid dynamics simulation, a BP neural network model, and the RRT* algorithm provides a system to plan low-risk paths for commuters. This work proposes artificial-intelligence-based models for calculating the exposure risk to traffic-related pollutants (PM2.5) and choosing a low-risk commuting path to ensure healthy travel.
Funder
Natural Science Foundation of Shandong Province
Subject
Building and Construction,Civil and Structural Engineering,Architecture
Cited by
13 articles.
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